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In this paper, we propose a novel, exact border-based approach that provides an optimal solution for the hiding of sensitive frequent item sets by 1) minimally extending the original database by a synthetically generated database part—the database extension, 2) formulating the creation of the database extension as a constraint satisfaction problem, 3) mapping the constraint satisfaction problem to an equivalent binary integer programming problem, 4) exploiting underutilized synthetic transactions to proportionally increase the support of nonsensitive item sets, 5) minimally relaxing the constraint satisfaction problem to provide an approximate solution close to the optimal one when an ideal solution does not exist, and 6) using a partitioning in the universe of the items to increase the efficiency of the proposed hiding algorithm. Extending the original database for sensitive item set hiding is proved to provide optimal solutions to an extended set of hiding problems compared to previous approaches and to provide solutions of higher quality. Moreover, the application of binary integer programming enables the simultaneous hiding of the sensitive item sets and thus allows for the identification of globally optimal solutions. [ABSTRACT FROM AUTHOR]

The class-imbalance problem is the problem of learning a classification rule from data that are skewed in favor of one class. On these data sets, traditional learning techniques tend to overlook the less numerous classes, at the advantage of the majority class. However, the minority class is often the most interesting one for the task at hand. For this reason, the class-imbalance problem has received increasing attention in the last few years. In the present paper, we point the attention of the reader to a learning algorithm for the minimization of the average misclassification risk. In contrast to some popular class-imbalance learning methods, this method has its roots in statistical decision theory. A particular interesting characteristic is that when class distributions are unknown, the method can work by resorting to stochastic gradient algorithm. We study the behavior of this algorithm on imbalanced data sets, demonstrating that this principled approach allows to obtain better classification performances compared to the principal methods proposed in the literature. [ABSTRACT FROM AUTHOR]

Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points that are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. In this paper, a mechanism named MAximal Resemblance Data Labeling (abbreviated as MARDL) is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering representative, namely, N-Nodeset Importance Representative(abbreviated as NNIR), which represents clusters by the importance of the combinations of attribute values. MARDL has two advantages: 1) MARDL exhibits high execution efficiency, and 2) MARDL can achieve high intracluster similarity and low intercluster similarity, which are regarded as the most important properties of clusters, thus benefiting the analysis of cluster behaviors. MARDL is empirically validated on real and synthetic data sets and is shown to be significantly more efficient than prior works while attaining results of high quality. [ABSTRACT FROM AUTHOR]

In recent years, a number of indirect data collection methodologies have led to the proliferation of uncertain data. Such databases are much more complex because of the additional challenges of representing the probabilistic information. In this paper, we provide a survey of uncertain data mining and management applications. We will explore the various models utilized for uncertain data representation. In the field of uncertain data management, we will examine traditional database management methods such as join processing, query processing, selectivity estimation, OLAP queries, and indexing. In the field of uncertain data mining, we will examine traditional mining problems such as frequent pattern mining, outlier detection, classification, and clustering. We discuss different methodologies to process and mine uncertain data in a variety of forms. [ABSTRACT FROM AUTHOR]

This paper presents the Mine index, a general and compact structure which provides tight integration of item set extraction in a relational DBMS. Since no constraint is enforced during the index creation phase, lMine provides a complete representation of the original database. To reduce the I/O cost, data accessed together during the same extraction phase are clustered on the same disk block. The IMine index structure can be efficiently exploited by different item set extraction algorithms. In particular, IMine data access methods currently support the FP-growth and LCM v.2 algorithms, but they can straightforwardly support the enforcement of various constraint categories. The IMine index has been integrated into the PostgreSQL DBMS and exploits its physical level access methods. Experiments, run for both sparse and dense data distributions, show the efficiency of the proposed index and its linear scalability also for large data sets. Item set mining supported by the IMine index shows performance always comparable with, and often (especially for low supports) better than, state-of-the-art algorithms accessing data on flat file. [ABSTRACT FROM AUTHOR]

Association rule mining, originally proposed for market basket data, has potential applications in many areas. Spatial data, such as remote sensed imagery (RSI) data, is one of the promising application areas. Extracting interesting patterns and rules from spatial data sets, composed of images and associated ground data, can be of importance in precision agriculture, resource discovery, and other areas. However, in most cases, the sizes of the spatial data sets are too large to be mined in a reasonable amount of time using existing algorithms. In this paper, we propose an efficient approach to derive association rules from spatial data using Peano Count Tree (P-tree) structure. P-tree structure provides a lossless and compressed representation of spatial data. Based on P-trees, an efficient association rule mining algorithm PARM with fast support calculation and signifi- cant pruning techniques is introduced to improve the efficiency of the rule mining process. The P-tree based Association Rule Mining (PARM) algorithm is implemented and compared with FP-growth and Apriori algorithms. Experimental results showed that our algorithm is superior for association rule mining on RSI spatial data. [ABSTRACT FROM AUTHOR]

To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise Linear Approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the runtime than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods. [ABSTRACT FROM AUTHOR]

The discovery of information encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually encoded in patterns frequently occurring in the sequences, which are also called motifs. In fact, motif discovery has received much attention in the literature, and several algorithms have already been proposed, which are specifically tailored to deal with motifs exhibiting some kinds of "regular structure." Motivated by biological observations, this paper focuses on the mining of loosely structured motifs, i.e., of more general kinds of motif where several "exceptions" may be tolerated in pattern repetitions. To this end, an algorithm exploiting data structures conceived to efficiently handle pattern variabilities is presented and analyzed. Furthermore, a randomized variant with linear time and space complexity is introduced, and a theoretical guarantee on its performances is proven. Both algorithms have been implemented and tested on real data sets. Despite the ability of mining very complex kinds of pattern, performance results evidence a genome-wide applicability of the proposed techniques. [ABSTRACT FROM AUTHOR]

Impact-targeted activities are rare but they may have a significant impact on the society. For example, isolated terrorism activities may lead to a disastrous event, threatening the national security. Similar issues can also be seen in many other areas. Therefore, it is important to identify such particular activities before they lead to having a significant impact to the world. However, it is challenging to mine impact-targeted activity patterns due to their imbalanced structure. This paper develops techniques for discovering such activity patterns. First, the complexities of mining imbalanced impact-targeted activities are analyzed. We then discuss strategies for constructing impact-targeted activity sequences. Algorithms are developed to mine frequent positive-impact-oriented (P → T¯) and negative-impact-oriented (P → T) activity patterns, sequential impact-contrasted activity patterns (P is frequently associated with both patterns P → T and P → T¯ in separated data sets), and sequential impact-reversed activity patterns (both P → T and PQ → T¯ are frequent). Activity impact modeling is also studied to quantify the pattern impact on business outcomes. Social security debt-related activity data is used to test the proposed approaches. The outcomes show that they are promising for information and security informatics (ISI) applications to identify impact-targeted activity patterns in imbalanced data. [ABSTRACT FROM AUTHOR]

Real-world data mining deals with noisy information sources where data collection inaccuracy, device limitations, data transmission and discretization errors, or man-made perturbations frequently result in imprecise or vague data. Two common practices are to adopt either data cleansing approaches to enhance the data consistency or simply take noisy data as quality sources and feed them into the data mining algorithms. Either way may substantially sacrifice the mining performance. In this paper, we consider an error-aware (EA) data mining design, which takes advantage of statistical error information (such as noise level and noise distribution) to improve data mining results. We assume that such noise knowledge is available in advance, and we propose a solution to incorporate it into the mining process. More specifically, we use noise knowledge to restore original data distributions, which are further used to rectify the model built from noise-corrupted data. We materialize this concept by the proposed EA naive Bayes classification algorithm. Experimental comparisons on real-world datasets will demonstrate the effectiveness of this design. [ABSTRACT FROM AUTHOR]

A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In this paper, we propose a signature-based indexing method to optihiize the storage and retrieval of a large collection of relative temporal patterns. [ABSTRACT FROM AUTHOR]

Ensemble methods have proven to be highly effective in improving the performance of base learners under most circumstances. In this paper, we propose a new algorithm that combines the merits of some existing techniques, namely, bagging, arcing, and stacking. The basic structure of the algorithm resembles bagging. However, the misclassification cost of each training point is repeatedly adjusted according to its observed out-of-bag vote margin. In this way, the method gains the advantage of arcing-building the classifier the ensemble needs-without fixating on potentially noisy points. Computational experiments show that this algorithm performs consistently better than bagging and arcing with linear and nonlinear base classifiers. In view of the characteristics of bacing, a hybrid ensemble learning strategy, which combines bagging and different versions of bacing, is proposed and studied empirically. [ABSTRACT FROM AUTHOR]

Association rule mining is a key issue in data mining. However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. In this paper, we introduce a new measure w-support, which does not require preassigned weights. It takes the quality of transactions into consideration using link-based models. A fast miming algorithm is given, and a large amount of experimental results are presented. [ABSTRACT FROM AUTHOR]

The inner product measures how closely two feature vectors are related. It is an important primitive for many popular data mining tasks, for example, clustering, classification, correlation computation, and decision tree construction. If the entire data set is available at a single site, then computing the inner product matrix and identifying the top (in terms of magnitude) entries is trivial. However, in many real-world scenarios, data is distributed across many locations and transmitting the data to a central server would be quite communication intensive and not scalable. This paper presents an approximate local algorithm for identifying top-i inner products among pairs of feature vectors in a large asynchronous distributed environment such as a peer-to-peer (P2P) network. We develop a probabilistic algorithm for this purpose using order statistics and the Hoeffding bound. We present experimental results to show the effectiveness and scalability of the algorithm. Finally, we demonstrate an application of this technique for interest-based community formation in a P2P environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel large margin classifier, called the maxi-min margin machine (M4). This model learns the decision boundary both locally and globally. In comparison, other large margin classifiers construct separating hyperplanes only either locally or globally. For example, a state-of-the-art large margin classifier, the support vector machine (SVM), considers data only locally, while another significant model, the minimax probability machine (MPM), focuses on building the decision hyperplane exclusively based on the global information. As a major contribution, we show that SVM yields the same solution as M4 when data satisfy certain conditions, and MPM can be regarded as a relaxation model of M4. Moreover, based on our proposed local and global view of data, another popular model, the linear discriminant analysis, can easily be interpreted and extended as well. We describe the M4 model definition, provide a geometrical interpretation, present theoretical justifications, and propose a practical sequential conic programming method to solve the optimization problem. We also show how to exploit Mercer kernels to extend M4 for nonlinear classifications. Furthermore, we perform a series of evaluations on both synthetic data sets and real-world benchmark data sets. Comparison with SVM and MPM demonstrates the advantages of our new model. [ABSTRACT FROM AUTHOR]

In this paper, we present a recursive algorithm for extracting classification rules from feedforward neural networks (NNs) that have been trained on data sets having both discrete and continuous attributes. The novelty of this algorithm lies in the conditions of the extracted rules: the rule conditions involving discrete attributes are disjoint from those involving continuous attributes. The algorithm starts by first generating rules with discrete attributes only to explain the classification process of the NN. If the accuracy of a rule with only discrete attributes is not satisfactory, the algorithm refines this rule by recursively generating more rules with discrete attributes not already present in the rule condition, or by generating a hyperplane involving only the continuous attributes. We show that for three real-life credit scoring data sets, the algorithm generates rules that are not only more accurate but also more comprehensible than those generated by other NN rule extraction methods. [ABSTRACT FROM AUTHOR]

We study a new data mining problem concerning the discovery of frequent agreement subtrees (FASTs) from a set of phylogenetic trees. A phylogenetic tree, or phylogeny, is an unordered tree in which the order among siblings is unimportant. Furthermore, each leaf in the tree has a label representing a taxon (species or organism) name, whereas internal nodes are unlabeled. The tree may have a root, representing the common ancestor of all species in the tree, or may be unrooted. An unrooted phylogeny arises due to the lack of sufficient evidence to infer a common ancestor of the taxa in the tree. The FAST problem addressed here is a natural extension of the maximum agreement subtree (MAST) problem widely studied in the computational phylogenetics community. The paper establishes a framework for tackling the FAST problem for both rooted and unrooted phylogenetic trees using data mining techniques. We first develop a novel canonical form for rooted trees together with a phylogeny-aware tree expansion scheme for generating candidate subtrees level by level. Then, we present an efficient algorithm to find all FASTs in a given set of rooted trees, through an Apriori-like approach. We show the correctness and completeness of the proposed method. Finally, we discuss the extensions of the techniques to unrooted trees. Experimental results demonstrate that the proposed methods work well, and are capable of finding interesting patterns in both synthetic data and real phylogenetic trees. [ABSTRACT FROM AUTHOR]

A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic c-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. It incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy c-means and the coincident clusters of PCM. The concept of crisp lower bound and fuzzy boundary of a class, which is introduced in the RFPCM, enables efficient selection of cluster prototypes. The algorithm is generalized in the sense that all existing variants of c-means algorithms can be derived from the proposed algorithm as a special case. Several quantitative indices are introduced based on rough sets for the evaluation of performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated both qualitatively and quantitatively on a set of real-life data sets. [ABSTRACT FROM AUTHOR]

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets. [ABSTRACT FROM AUTHOR]

Text categorization systems often use machine learning techniques to induce document classifiers from preclassified examples. The fact that each example document belongs to many classes often leads to very high computational costs that sometimes grow exponentially in the number of features. Seeking to reduce these costs, we explored the possibility of running a ‘baseline induction algorithm’ separately for subsets of features, obtaining a set of classifiers to be combined. For the specific case of classifiers that return not only class labels but also confidences in these labels, we investigate here a few alternative fusion techniques, including our own mechanism that was inspired by the Dempster-Shafer Theory. The paper describes the algorithm and, in our specific case study, compares its performance to that of more traditional mechanisms. [ABSTRACT FROM AUTHOR]

The traditional setting of supervised learning requires a large amount of labeled training examples in order to achieve good generalization. However, in many practical applications, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semisupervised learning has attracted much attention. Previous research on semisupervised learning mainly focuses on semisupervised classification. Although regression is almost as important as classification, semisupervised regression is largely understudied. In particular, although cotraining is a main paradigm in semisupervised learning, few works has been devoted to cotraining-style semisupervised regression algorithms. In this paper, a cotraining-style semisupervised regression algorithm, that is, COREG, is proposed. This algorithm uses two regressors, each labels the unlabeled data for the other regressor, where the confidence in labeling an unlabeled example is estimated through the amount of reduction in mean squared error over the labeled neighborhood of that example. Analysis and experiments show that COREG can effectively exploit unlabeled data to improve regression estimates. [ABSTRACT FROM AUTHOR]

The ability to transfer knowledge learned in one environment in order to improve performance in a different environment is one of the hallmarks of human intelligence. Insights into human transfer learning help us to design computer-based agents that can better adapt to new environments without the need for substantial reprogramming. In this paper, we study the transfer of knowledge by humans playing various scenarios in a graphically realistic urban setting that are specifically designed to test various levels of transfer. We determine the amount and type of transfer that is being performed based on the performance of trained and untrained human players. In addition, we use a graph-based relational learning algorithm to extract patterns from player graphs. These analyses reveal that indeed humans are transferring knowledge from one set of games to another and the amount and type of transfer varies according to player experience and scenario complexity. The results of this analysis help us understand the nature of human transfer in such environments and shed light on how we might endow computer-based agents with similar capabilities. The game simulator and human data collection also represent a significant testbed in which other Al capabilities can be tested and compared to human performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with the framework of frequent episode discovery in event sequences. A new temporal pattern, called the generalized episode, is defined, which extends this framework by incorporating event duration constraints explicitly into the pattern's definition. This new formalism facilitates extension of the technique of episodes discovery to applications where data appears as a sequence of events that persist for different durations (rather than being instantaneous). We present efficient algorithms for episode discovery in this new framework. Through extensive simulations, we show the expressive power of the new formalism. We also show how the duration constraint possibilities can be used as a design choice to properly focus the episode discovery process. Finally, we briefly discuss some interesting results obtained on data from manufacturing plants of General Motors. [ABSTRACT FROM AUTHOR]

Abstract-This paper addresses the problem of finding frequent closed patterns (FCP5) from very dense data sets. We introduce two compressed hierarchical FCP mining algorithms: C-Miner and B-Miner. The two algorithms compress the original mining space, hierarchically partition the whole mining task into independent subtasks, and mine each subtask progressively. The two algorithms adopt different task partitioning strategies: C-Miner partitions the mining task based on Compact Matrix Division, whereas B-Miner partitions the task based on Base Rows Projection. The compressed hierarchical mining algorithms enhance the mining efficiency and facilitate a progressive refinement of results. Moreover, because the subtasks can be mined independently, C-Miner and B-Miner can be readily paralleled without incurring significant communication overhead. We have implemented C-Miner and B-Miner, and our performance study on synthetic data sets and real dense microarray data sets shows their effectiveness over existing schemes. We also report experimental results on parallel versions of these two methods. [ABSTRACT FROM AUTHOR]

This paper presents a new k-means type algorithm for clustering high-dimensional objects in subspaces. In high-dimensional data, clusters of objects often exist in subspaces rather than in the entire space. For example, in text clustering, clusters of documents of different topics are categorized by different subsets of terms or keywords. The keywords for one cluster may not occur in the documents of other clusters. This is a data sparsity problem faced in clustering high-dimensional data. In the new algorithm, we extend the k-means clustering process to calculate a weight for each dimension in each cluster and use the weight values to identify the subsets of important dimensions that categorize different clusters. This is achieved by including the weight entropy in the objective function that is minimized in the k-means clustering process. An additional step is added to the k-means clustering process to automatically compute the weights of all dimensions in each cluster. The experiments on both synthetic and real data have shown that the new algorithm can generate better clustering results than other subspace clustering algorithms. The new algorithm is also scalable to large data sets. [ABSTRACT FROM AUTHOR]

This paper targets the identification of outlying sensors (that is, outlying reading sensors) and the detection of the reach of events in sensor networks. Typical applications include the detection of the transportation front line of some vegetation or animalcule's growth over a certain geographical region. We propose and analyze two novel algorithms for outlying sensor identification and event boundary detection. These algorithms are purely localized and, thus, scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify outlying sensors with a high accuracy and a low false alarm rate when as many as 20 percent sensors report outlying readings. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs—a dramatic improvement over existing work, which takes only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as "events" can be modeled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining. [ABSTRACT FROM AUTHOR]

Previous studies have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent patterns but only the closed ones because the latter leads to not only a more compact yet complete result set but also better efficiency. However, most of the previously developed closed pattern mining algorithms work under the candidate maintenance-and-test paradigm, which is inherently costly in both runtime and space usage when the support threshold is low or the patterns become long. In this paper, we present BIDE, an efficient algorithm for mining frequent closed sequences without candidate maintenance. It adopts a novel sequence closure checking scheme called BI-Directional Extension and prunes the search space more deeply compared to the previous algorithms by using the BackScan pruning method. A thorough performance study with both sparse and dense, real, and synthetic data sets has demonstrated that BIDE significantly outperforms the previous algorithm: It consumes an order(s) of magnitude less memory and can be more than an order of magnitude faster. It is also linearly scalable in terms of database size. [ABSTRACT FROM AUTHOR]

In most pattern recognition algorithms, amino acids cannot be used directly as inputs since they are nonnumerical variables. They, therefore, need encoding prior to input. In this regard, bio-basis function maps a nonnumerical sequence space to a numerical feature space. It is designed using an amino acid mutation matrix. One of the important issues for the bio-basis function is how to select the minimum set of bio-bases with maximum information. In this paper, we describe an algorithm, termed as rough-fuzzy c-medoids (RFCMdd) algorithm, to select the most informative bio-bases. It is comprised of a judicious integration of the principles of rough sets, fuzzy sets, the c-medoids algorithm, and the amino acid mutation matrix. While the membership function of fuzzy sets enables efficient handling of overlapping partitions, the concept of lower and upper bounds of rough sets deals with uncertainty, vagueness, and incompleteness in class definition. The concept of crisp lower bound and fuzzy boundary of a class, introduced in RFCMdd, enables efficient selection of the minimum set of the most informative bio-bases. Some new indices are introduced for evaluating quantitatively the quality of selected bio-bases. The effectiveness of the proposed algorithm, along with a comparison with other algorithms, has been demonstrated on different types of protein data sets. [ABSTRACT FROM AUTHOR]

In this paper, we examine the scope of validity of the explicit self-organizing map (SOM) magnification control scheme of Bauer et al. (1996) on data for which the theory does not guarantee success, namely data that are n-dimensional, n ≥ 2, and whose components in the different dimensions are not statistically independent. The Bauer et al. algorithm is very attractive for the possibility of faithful representation of the probability density function (pdf) of a data manifold, or for discovery of rare events, among other properties. Since theoretically unsupported data of higher dimensionality and higher complexity would benefit most from the power of explicit magnification control, we conduct systematic simulations on ‘forbidden’ data. For the unsupported n = 2 cases that we investigate, the simulations show that even though the magnification exponent αachieved achieved by magnification control is not the same as the desired αdesired, αachieved systematically follows αdesired with a slowly increasing positive offset. We show that for simple synthetic higher dimensional data information, theoretically optimum pdf matching (αachieved = 1) can be achieved, and that negative magnification has the desired effect of improving the detectability of rare classes. In addition, we further study theoretically unsupported cases with real data. [ABSTRACT FROM AUTHOR]

Common substring problems allowing errors are known to be NP-hard. The main challenge of the problems lies in the combinatorial explosion of potential candidates. In this paper, we propose and study a Generalized Center String (GCS) problem, where not only all models (center strings) of any length, but also the positions of all their (degenerative) instances in input sequences are searched for. Inspired by frequent pattern mining techniques in data mining field, we present an exact and efficient method to solve GCS. First, a highly parallelized TAlE-like structure, consensus tree, is proposed. Based on this structure, we present three Bpriori algorithms step by step. Bpriori algorithms can solve GCS with reasonable time and/or space complexities. We have proved that GCS is fixed parameter tractable with respect to fixed symbol set size and fixed length of input sequences. Experiment results on both artificial and real data have shown the correctness of the algorithms and the validity of our complexity analysis. A comparison with some current algorithms for solving Common Approximate Substring problems is also given. [ABSTRACT FROM AUTHOR]

We explore in this paper a novel sampling algorithm, referred to as algorithm PAS (standing for Proportion Approximation Sampling), to generate a high-quality online sample with the desired sample rate. The sampling quality refers to the consistency between the population proportion and the sample proportion of each categorical value in the database. Note that the state-of-the-art sampling algorithm to preserve the sampling quality has to examine the population proportion of each categorical value in a pilot sample a priori and is thus not applicable to incremental mining applications. To remedy this, algorithm PAS adaptively determines the inclusion probability of each incoming tuple in such a way that the sampling quality can be sequentially preserved while also guaranteeing the sample rate close to the user specified one. Importantly, PAS not only guarantees the proportion consistency of each categorical value but also excellently preserves the proportion consistency of multivariate statistics, which will be significantly beneficial to various data mining applications. For better execution efficiency, we further devise an algorithm, called algorithm EQAS (standing for Efficient Quality-Aware Sampling), which integrates PAS and random sampling to provide the flexibility of striking a compromise between the sampling quality and the sampling efficiency. As validated in experimental results on real and synthetic data, algorithm PAS can stably provide high-quality samples with corresponding computational overhead, whereas algorithm EQAS can flexibly generate samples with the desired balance between sampling quality and sampling efficiency. In addition, while applying the sample generated by algorithms PAS and EQAS to incremental mining applications, a significant efficiency improvement can be obtained without compromising the resulting precision, showing the prominent advantage of both proposed algorithms to be the quality-aware sampling means for incremental mining applications. [ABSTRACT FROM AUTHOR]

Recently, relevance feedback (RF) in content-based image retrieval (CBIR) has been implemented as an online binary classifier to separate the positive samples from the negative samples, where both sets of samples are labeled by the user. In many applications, it is reasonable to assume that all the positive samples are alike and thus that the region of the feature space occupied by the positive samples can be described by a single hypersurface. However, for the negative samples, previous RF methods either treat each one of the negative samples as an isolated point or assume the whole negative set can be described by a single convex hypersurface. In this paper, we argue that these treatments of the negative samples are not sound. Our belief is all positive samples are included in a set and the negative samples split into a small number of subsets, each one of which has a simple distribution. Therefore, we first cluster the negative samples into several groups; for each such negative group, we build a marginal convex machine (MCM) subclassifier between it and the single positive group which results in a series of subclassifiers. These subclassifiers are then incorporated into a biased MOM (BMCM) for RF. Experiments were carried out to prove the advantages of BMCM-based RF over previous methods for RF. [ABSTRACT FROM AUTHOR]

Schema matching is a basic operation of data integration, and several tools for automating it have been proposed and evaluated in the database community. Research in this area reveals that there is no single schema matcher that is guaranteed to succeed in finding a good mapping for all possible domains and, thus, an ensemble of schema matchers should be considered. In this paper, we introduce schema metamatching, a general framework for composing an arbitrary ensemble of schema matchers and generating a list of best ranked schema mappings. Informally, schema metamatching stands for computing a "consensus" ranking of alternative mappings between two schemata, given the "individual" graded rankings provided by several schema matchers. We introduce several algorithms for this problem, varying from adaptations of some standard techniques for general quantitative rank aggregation to novel techniques specific to the problem of schema matching, and to combinations of both. We provide a formal analysis of the applicability and relative performance of these algorithms and evaluate them empirically on a set of real-world schemata. [ABSTRACT FROM AUTHOR]

In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data could unveil important information to the data analyst. Existing approaches for discovering periodic patterns focus on symbol sequences. However, these methods cannot directly be applied to a spatiotemporal sequence because of the fuzziness of spatial locations in the sequence. In this paper, we define the problem of mining periodic patterns in spatiotemporal data and propose an effective and efficient algorithm for retrieving maximal periodic patterns. In addition, we study two interesting variants of the problem. The first is the retrieval of periodic patterns that are frequent only during a continuous subinterval of the whole history. The second problem is the discovery of periodic patterns, whose instances may be shifted or distorted. We demonstrate how our mining technique can be adapted for these variants. Finally, we present a comprehensive experimental evaluation, where we show the effectiveness and efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

lThis correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical Objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework. [ABSTRACT FROM AUTHOR]

Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. The results demonstrate that the MCES method has following strong advantages: 1) ability to identify correlated and irrelevant features based on weight ranking, 2) application to both nonlinear system estimation and classification tasks, and 3) independence of the underlying induction algorithms, used to derive the performance measures. [ABSTRACT FROM AUTHOR]

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real- world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi et al. to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced. [ABSTRACT FROM AUTHOR]

As OLAP engines are widely used to support multidimensional data analysis, it is desirable to support in data cubes advanced statistical measures, such as regression and filtering, in addition to the traditional simple measures such as count and average. Such new measures will allow users to model, smooth, and predict the trends and patterns of data. Existing algorithms for simple distributive and algebraic measures are inadequate for efficient computation of statistical measures in a multidimensional space. In this paper, we propose a fundamentally new class of measures, compressible measures, in order to support efficient computation of the statistical models. For compressible measures, we compress each cell into an auxiliary matrix with a size independent of the number of tuples. We can then compute the statistical measures for any data cell from the compressed data of the lower-level cells without accessing the raw data. Time- and space-efficient lossless aggregation formulae are derived for regression and filtering measures. Our analytical and experimental studies show that the resulting system, regression cube, substantially reduces the memory usage and the overall response time for statistical analysis of multidimensional data. [ABSTRACT FROM AUTHOR]

Whereas data mining in structured data focuses on frequent data values, in semistructured and graph data mining, the issue is frequent labels and common specific topologies. Here, the structure of the data is just as important as its content. We study the problem of discovering typical patterns of graph data, a task made difficult because of the complexity of required subtasks, especially subgraph isomorphism. In this paper, we propose a newApriori-based algorithm for mining graph data, where the basic building blocks are relatively large, disjoint paths. The algorithm is proven to be sound and complete. Empirical evidence shows practical advantages of our approach for certain categories of graphs. [ABSTRACT FROM AUTHOR]

Mining knowledge about ordering from sequence data is an important problem with many applications, such as bioinformatics, Web mining, network management, and intrusion detection. For example, if many customers follow a partial order in their purchases of a series of products, the partial order can be used to predict other related customers' future purchases and develop marketing campaigns. Moreover, some biological sequences (e.g., microarray data) can be clustered based on the partial orders shared by the sequences. Given a set of items, a total order of a subset of items can be represented as a string. A string database is a multiset of strings. In this paper, we identify a novel problem of mining frequent closed partial orders from strings. Frequent closed partial orders capture the nonredundant and interesting ordering information from string databases. Importantly, mining frequent closed partial orders can discover meaningful knowledge that cannot be disclosed by previous data mining techniques. However, the problem of mining frequent closed partial orders is challenging. To tackle the problem, we develop Frecpo (for Frequent closed partial order), a practically efficient algorithm for mining the complete set of frequent closed partial orders from large string databases. Several interesting pruning techniques are devised to speed up the search. We report an extensive performance study on both real data sets and synthetic data sets to illustrate the effectiveness and the efficiency of our approach. [ABSTRACT FROM AUTHOR]

Many studies have shown that rule-based classifiers perform well in classifying categorical and sparse high-dimensional databases. However, a fundamental limitation with many rule-based classifiers is that they find the rules by employing various heuristic methods to prune the search space and select the rules based on the sequential database covering paradigm. As a result, the final set of rules that they use may not be the globally best rules for some instances in the training database. To make matters worse, these algorithms fail to fully exploit some more effective search space pruning methods in order to scale to large databases. In this paper, we present a new classifier, HARMONY, which directly mines the final set of classification rules. HARMONY uses an instance-centric rule- generation approach and it can assure that, for each training instance, one of the highest-confidence rules covering this instance is included in the final rule set, which helps in improving the overall accuracy of the classifier. By introducing several novel search strategies and pruning methods into the rule discovery process, HARMONY also has high efficiency and good scalability. Our thorough performance study with some large text and categorical databases has shown that HARMONY outperforms many well-known classifiers in terms of both accuracy and computational efficiency and scales well with regard to the database size. [ABSTRACT FROM AUTHOR]

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., Backpropagation for Multilabel Learning, is proposed. It is derived from the popular Backpropagation algorithm through employing a novel error function capturing the characteristics of multilabel learning, i.e., the labels belonging to an instance should be ranked higher than those not belonging to that instance. Applications to two real-world multilabel learning problems, i.e., functional genomics and text categorization, show that the performance of BP-MLL is superior to that of some well-established multilabel learning algorithms. [ABSTRACT FROM AUTHOR]

Given a set D of objects, a reverse nearest neighbor (RNN) query returns the objects q in D such that o is closer to a query object q than to any other object in D, according to a certain similarity metric. The existing ANN solutions are not sufficient because they either 1) rely on precomputed information that is expensive to maintain in the presence of updates or 2) are applicable only when the data consists of ‘Euclidean objects’ and similarity is measured using the L2 norm. In this paper, we present the first algorithms for efficient RNN search in generic metric spaces. Our techniques require no detailed representations of objects, and can be applied as long as their mutual distances can be computed and the distance metric satisfies the triangle inequality. We confirm the effectiveness of the proposed methods with extensive experiments. [ABSTRACT FROM AUTHOR]

Relational databases are the most popular repository for structured data, and is thus one of the richest sources of knowledge in the world. In a relational database, multiple relations are linked together via entity-relationship links. Multirelational classification is the procedure of building a classifier based on information stored in multiple relations and making predictions with it. Existing approaches of Inductive Logic Programming (recently, also known as Relational Mining) have proven effective with high accuracy in multirelational classification. Unfortunately, most of them suffer from scalability problems with regard to the number of relations in databases. In this paper, we propose a new approach, called CrossMine, which includes a set of novel and powerful methods for multirelational classification, including 1) tuple ID propagation, an efficient and flexible method for virtually joining relations, which enables convenient search among different relations, 2) new definitions for predicates and decision-tree nodes, which involve aggregated information to provide essential statistics for classification, and 3) a selective sampling method for improving scalability with regard to the number of tuples. Based on these techniques, we propose two scalable and accurate methods for multirelational classification: CrossMine-Rule, a rule-based method and CrossMine-Tree, a decision-tree-based method. Our comprehensive experiments on both real and synthetic data sets demonstrate the high scalability and accuracy of the CrossMine approach. [ABSTRACT FROM AUTHOR]

Incorporating constraints into frequent itemset mining not only improves data mining efficiency, but also leads to concise and meaningful results. In this paper, a framework for closed constrained gradient itemset mining in retail databases is proposed by introducing the concept of gradient constraint into closed itemset mining. A tailored version of CLOSET+, LCLOSET, is first briefly introduced, which is designed for efficient closed itemset mining from sparse databases. Then, a newly proposed weaker but antimonotone measure, top-X average measure, is proposed and can be adopted to prune search space effectively. Experiments show that a combination of LCLOSET and the top-X average pruning provides an efficient approach to mining frequent closed gradient itemsets. [ABSTRACT FROM AUTHOR]

Support Vector Machines (SVMs) have been adopted by many data mining and information-retrieval applications for learning a mining or query concept, and then retrieving the ‘top-k’ best matches to the concept. However, when the data set is large, naively scanning the entire data set to find the top matches is not scalable. In this work, we propose a kernel indexing strategy to substantially prune the search space and, thus, improve the performance of top-k queries. Our kernel indexer (KDX) takes advantage of the underlying geometric properties and quickly converges on an approximate set of top-k instances of interest. More importantly, once the kernel (e.g., Gaussian kernel) has been selected and the indexer has been constructed, the indexer can work with different kernel-parameter settings (e.g., γ and σ) without performance compromise. Through theoretical analysis and empirical studies on a wide variety of data sets, we demonstrate KDX to be very effective. An earlier version of this paper appeared in the 2005 SIAM International Conference on Data Mining [24]. This version differs from the previous submission in providing a detailed cost analysis under different scenarios, specifically designed to meet the varying needs of accuracy, speed, and space requirements, developing an approach for insertion and deletion of instances, presenting the specific computations as well as the geometric properties used in performing the same, and providing detailed algorithms for each of the operations necessary to create and use the index structure. [ABSTRACT FROM AUTHOR]

Classification of large data sets is an important data mining problem that has wide applications. Jumping Emerging Patterns (JEPs) are those itemsets whose supports increase abruptly from zero in one data set to nonzero in another data set. In this paper, we propose a fast, accurate, and less complex classifier based on a subset of JEPs, called Strong Jumping Emerging Patterns (SJEPs). The support constraint of SJEP removes potentially less useful JEPs while retaining those with high discriminating power. Previous algorithms based on the manipulation of border [1] as well as consEPMiner [2] cannot directly mine SJEPs. Here, we present a new tree-based algorithm for their efficient discovery. Experimental results show that: 1) the training of our classifier is typically 10 times faster than earlier approaches, 2) our classifier uses much fewer patterns than the JEP-Classifier [3] to achieve a similar (and, often, improved) accuracy, and 3) in many cases, it is superior to other state-of-the-art classification systems such as Naive Bayes, CBA, C4.5, and bagged and boosted versions of C4.5. We argue that SJEPs are high-quality patterns which possess the most differentiating power. As a consequence, they represent sufficient information for the construction of accurate classifiers. In addition, we generalize these patterns by introducing Noise-tolerant Emerging Patterns (NEPs) and Generalized Noise-tolerant Emerging Patterns (GNEPs). Our tree-based algorithms can be adopted to easily discover these variations. We experimentally demonstrate that SJEPs, NEPs, and GNEPs are extremely useful for building effective classifiers that can deal well with noise. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM (Transaction Mapping) algorithm from hereon. In this algorithm, transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space and the counting of itemsets is performed by intersecting these interval lists in a depth-first order along the lexicographic tree. When the compression coefficient becomes smaller than the average number of comparisons for intervals intersection at a certain level, the algorithm switches to transaction id intersection. We have evaluated the algorithm against two popular frequent itemset mining algorithms, FP-growth and dEclat, using a variety of data sets with short and long frequent patterns. Experimental data show that the TM algorithm outperforms these two algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a data mining algorithm based on supervised clustering to learn data patterns and use these patterns for data classification. This algorithm enables a scalable incremental learning of patterns from data with both numeric and nominal variables. Two different methods of combining numeric and nominal variables in calculating the distance between clusters are investigated. In one method, separate distance measures are calculated for numeric and nominal variables, respectively, and are then combined into an overall distance measure. In another method, nominal variables are converted into numeric variables, and then a distance measure is calculated using all variables. We analyze the computational complexity, and thus, the scalability, of the algorithm, and test its performance on a number of data sets from various application domains. The prediction accuracy and reliability of the algorithm are analyzed, tested, and compared with those of several other data mining algorithms. [ABSTRACT FROM AUTHOR]

A drawback of traditional data-mining methods is that they do not leverage prior knowledge of users. In prior work, we proposed a method that could discover unexpected patterns in data by using domain knowledge in a systematic manner. In this paper, we present new methods for discovering a minimal set of unexpected patterns by combining the two independent concepts of minimality and unexpectedness, both of which have been well-studied in the KDD literature. We demonstrate the strengths of this approach experimentally using a case study in a marketing domain. [ABSTRACT FROM AUTHOR]