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For sequence classification, an important issue is to find discriminative features, where sequential pattern mining (SPM) is often used to find frequent patterns from sequences as features. To improve classification accuracy and pattern interpretability, contrast pattern mining emerges to discover patterns with high-contrast rates between different categories. To date, existing contrast SPM methods face many challenges, including excessive parameter selection and inefficient occurrences counting. To tackle these issues, this article proposes a top- k self-adaptive contrast SPM, which adaptively adjusts the gap constraints to find top- k self-adaptive contrast patterns (SCPs) from positive and negative sequences. One of the key tasks of the mining problem is to calculate the support (the number of occurrences) of a pattern in each sequence. To support efficient counting, we store all occurrences of a pattern in a special array in a Nettree, an extended tree structure with multiple roots and multiple parents. We employ the array to calculate the occurrences of all its superpatterns with one-way scanning to avoid redundant calculation. Meanwhile, because the contrast SPM problem does not satisfy the Apriori property, we propose Zero and Less strategies to prune candidate patterns and a Contrast-first mining strategy to select patterns with the highest contrast rate as the prefix subpattern and calculate the contrast rate of all its superpatterns. Experiments validate the efficiency of the proposed algorithm and show that contrast patterns significantly outperform frequent patterns for sequence classification. The algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/SCP-Miner.

The importance of searching biomedical literature for drug interaction and side-effects is apparent. Current digital libraries (e.g., PubMed) suffer infrequent tagging and metadata annotation updates. Such limitations cause absence of linking literature to new scientific evidence. This demonstrates a great deal of challenges that stand in the way of scientists when searching biomedical repositories. In this paper, we present a network mining approach that provides a bridge for linking and searching drug-related literature. Our contributions here are two fold: (1) an efficient algorithm called HashPairMiner to address the run-time complexity issues demonstrated in its predecessor algorithm: HashnetMiner, and (2) a database of discoveries hosted on the web to facilitate literature search using the results produced by HashPairMiner. Though the K-H network model and the HashPairMiner algorithm are fairly young, their outcome is evidence of the considerable promise they offer to the biomedical science community in general and the drug research community in particular., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Patterns/subsequences frequently appearing in sequences provide essential knowledge for domain experts, such as molecular biologists, to discover rules or patterns hidden behind the data. Due to the inherent complex nature of the biological data, patterns rarely exactly reproduce and repeat themselves, but rather appear with a slightly different form in each of its appearances. A gap constraint (In this paper, a gap constraint (also referred to as a wildcard) is a character that can be substituted for any character predefined in an alphabet.) provides flexibility for users to capture useful patterns even if their appearances vary in the sequences. In order to find patterns, existing tools require users to explicitly specify gap constraints beforehand. In reality, it is often nontrivial or time-consuming for users to provide proper gap constraint values. In addition, a change made to the gap values may give completely different results, and require a separate time-consuming re-mining procedure. Therefore, it is desirable to automatically and efficiently find patterns without involving user-specified gap requirements. In this paper, we study the problem of frequent pattern mining without user-specified gap constraints and propose PMBC (namely P̲atternM̲ining from B̲iological sequences with wildcard C onstraints) to solve the problem. Given a sequence and a support threshold value (i.e. pattern frequency threshold), PMBC intends to discover all subsequences with their support values equal to or greater than the given threshold value. The frequent subsequences then form patterns later on. Two heuristic methods (one-way vs. two-way scans) are proposed to discover frequent subsequences and estimate their frequency in the sequences. Experimental results on both synthetic and real-world DNA sequences demonstrate the performance of both methods for frequent pattern mining and pattern frequency estimation., (Published by Elsevier Ltd.)

Many large organizations have multiple data sources, such as different branches of an interstate company. While putting all data together from different sources might amass a huge database for centralized processing, mining association rules at different data sources and forwarding the rules (rather than the original raw data) to the centralized company headquarter provides a feasible way to deal with multiple data source problems. In the meanwhile, the association rules at each data source may be required for that data source in the first instance, so association analysis at each data source is also important and useful. However, the forwarded rules from different data sources may be too many for the centralized company headquarter to use. This paper presents a weighting model for synthesizing high-frequency association rules from different data sources. There are two reasons to focus on high-frequency rules. First, a centralized company headquarter is interested in high-frequency rules because they are supported by most of its branches for corporate profitability. Second, high-frequency rules have larger chances to become valid rules in the union of all data sources. In order to extract high-frequency rules efficiently, a procedure of rule selection is also constructed to enhance the weighting model by coping with low-frequency rules. Experimental results show that our proposed weighting model is efficient and effective. Index Terms--Large databases, multiple data sources, association rules, synthesizing, weighting, rule selection.

After entering the big data era, a new term of 'big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed. [ABSTRACT FROM AUTHOR]

With the crowdsourcing of small tasks becoming easier, it is possible to obtain non-expert/imperfect labels at low cost. With low-cost imperfect labeling, it is straightforward to collect multiple labels for the same data items. This paper proposes strategies of utilizing these multiple labels for supervised learning, based on two basic ideas: majority voting and pairing. We show several interesting results based on our experiments. (i) The strategies based on the majority voting idea work well under the situation where the certainty level is high. (ii) On the contrary, the pairing strategies are more preferable under the situation where the certainty level is low. (iii) Among the majority voting strategies, soft majority voting can reduce the bias and roughness, and perform better than majority voting. (iv) Pairing can completely avoid the bias by having both sides (potentially correct and incorrect/noisy information) considered. Beta estimation is applied to reduce the impact of the noise in pairing. Our experimental results show that pairing with Beta estimation always performs well under different certainty levels. (v) All strategies investigated are labeling quality agnostic strategies for real-world applications, and some of them perform better than or at least very close to the gnostic strategies. [ABSTRACT FROM AUTHOR]

Online feature selection is a challenging topic in data mining. It aims to reduce the dimensionality of streaming features by removing irrelevant and redundant features in real time. Existing works, such as Alpha-investing and Online Streaming Feature Selection (OSFS), have been proposed to serve this purpose, but they have drawbacks, including low prediction accuracy and high running time if the streaming features exhibit characteristics such as low redundancy and high relevance. In this paper, we propose a novel algorithm about online streaming feature selection, named ConInd that uses a three-layer filtering strategy to process streaming features with the aim of overcoming such drawbacks. Through three-layer filtering, i.e., null-conditional independence, single-conditional independence, and multi-conditional independence, we can obtain an approximate Markov blanket with high accuracy and low running time. To validate the efficiency, we implemented the proposed algorithm and tested its performance on a prevalent dataset, i.e., NIPS 2003 and Causality Workbench. Through extensive experimental results, we demonstrated that ConInd offers significant performance improvements in prediction accuracy and running time compared to Alpha-investing and OSFS. ConInd offers 5.62% higher average prediction accuracy than Alpha-investing, with a 53.56% lower average running time compared to that for OSFS when the dataset is lowly redundant and highly relevant. In addition, the ratio of the average number of features for ConInd is 242% less than that for Alpha-investing. [ABSTRACT FROM AUTHOR]

Mining co-occurrence frequency patterns from multiple sequences is a hot topic in bioinformatics. Many seemingly disorganized constituents repetitively appear under different biological matrices, such as PAM250 and BLOSUM62, which are considered hidden frequent patterns (FPs). A hidden FP with both gap and flexible approximation operations (replacement, deletion or insertion) deepens the difficulty in discovering its true occurrences. To effectively discover co-occurrence FPs (Co-FPs) under these conditions, we design a mining algorithm (co-fp-miner) using the following steps: (1) a biological approximation scoring matrix is designed to discover various deformations of a single FP pattern; (2) a data-driven intersection tactic is used to generate candidate Co-FPs; (3) a deterministic Apriori-like rule is proposed to prune unnecessary Co-FPs; and (4) finally, we employ a backtracking matching scheme to validate true Co-FPs. The co-fp-miner algorithm is an unified framework for both exact and approximate mining on multiple sequences. Experiments on DNA and protein sequences demonstrate that co-fp-miner is more efficient on solutions, time and memory consumption than that of other peers. [ABSTRACT FROM AUTHOR]

Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover meaningful subsequences suitable for their own application domains, such as finding gene transcription sites from DNA sequences or discovering patterns for time series data classification. Due to the inherent complexity of sequence patterns, including the exponential candidate space with respect to pattern letters and gap constraints, to date, existing sequence pattern mining methods are either incomplete or do not support the Apriori property because the support ratio of a pattern may be greater than that of its subpatterns. Most importantly, patterns discovered by these methods are either too restrictive or too general and cannot represent underlying meaningful knowledge in the sequences. In this paper, we focus on a nonoverlapping sequence pattern mining task with gap constraints, where a nonoverlapping sequence pattern allows sequence letters to be flexibly and maximally utilized for pattern discovery. A new Apriori-based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed. NOSEP is a complete pattern mining algorithm, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence. Experimental results and comparisons on biology DNA sequences, time series data, and Gazelle datasets demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence patterns compared to other methods. [ABSTRACT FROM AUTHOR]

A comprehensive knowledge system reveals the intangible insights hidden in an information system by integrating information from multiple data sources in a synthetical manner. In this paper, we present a variable precision reduction theory, underpinned by two new concepts: 1) distribution tables and 2) genealogical binary trees. Sufficient and necessary conditions to extract comprehensive knowledge from a given information system are also presented and proven. A complete variable precision reduction algorithm is proposed, in which we introduce four important strategies, namely, distribution table abstracting, attribute rank dynamic updating, hierarchical binary classifying, and genealogical tree pruning. The completeness of our algorithm is proven theoretically and its superiority to existing methods for obtaining complete reducts is demonstrated experimentally. Finally, having obtaining the complete reduct set, we demonstrate how the relationships between the complete reduct set and the comprehensive knowledge system can be visualized in a double-layer lattice structure using Hasse diagrams. [ABSTRACT FROM PUBLISHER]

One of the prevalent studies on Topic Detection and Tracking (TDT) is topic evolution. With the emergence of Internet data, there is a clear need for intuitive and adaptive methods to analyze a series of evolutions. Most approaches completely depend on topic models and only focus on whether topics are changed while ignoring the degree of changes, resulting in poor quality topics and insensitivity of changes. In this paper, we propose a framework of topic evolution based on semantic connections which not only indicates the content similarity between documents but also shows the time decay for an adaptive number of topics and rapid responses to the changes of contents. Additionally, semantic connection features can be used to visualize topic evolution, which makes the analyses much easier. For empirical studies, three data sets in real applications are chosen to prove the effectiveness of our method, and the results show that our method has a better performance in reducing redundant topics, avoiding topic suppression, and discerning the vanishment of old topics and the appearance of new topics. [ABSTRACT FROM AUTHOR]

Graph classification is an important tool for analyzing data with structure dependency, where subgraphs are often used as features for learning. In reality, the dimension of the subgraphs crucially depends on the threshold setting of the frequency support parameter, and the number may become extremely large. As a result, subgraphs may be incrementally discovered to form a feature stream and require the underlying graph classifier to effectively discover representative subgraph features from the subgraph feature stream. In this paper, we propose a primal-dual incremental subgraph feature selection algorithm (ISF) based on a max-margin graph classifier. The ISF algorithm constructs a sequence of solutions that are both primal and dual feasible. Each primal-dual pair shrinks the dual gap and renders a better solution for the optimal subgraph feature set. To avoid bias of ISF algorithm on short-pattern subgraph features, we present a new incremental subgraph join feature selection algorithm (ISJF) by forcing graph classifiers to join short-pattern subgraphs and generate long-pattern subgraph features. We evaluate the performance of the proposed models on both synthetic networks and real-world social network data sets. Experimental results demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM PUBLISHER]

In this paper, we study a new problem of continuous learning from doubly-streaming data where both data volume and feature space increase over time. We refer to the doubly-streaming data as trapezoidal data streams and the corresponding learning problem as online learning from trapezoidal data streams. The problem is challenging because both data volume and data dimension increase over time, and existing online learning [1] , [2] , online feature selection [3] , and streaming feature selection algorithms [4] , [5] are inapplicable. We propose a new Online Learning with Streaming Features algorithm (OLSF for short) and its two variants, which combine online learning [1] , [2] and streaming feature selection [4] , [5] to enable learning from trapezoidal data streams with infinite training instances and features. When a new training instance carrying new features arrives, a classifier updates the existing features by following the passive-aggressive update rule [2] and updates the new features by following the structural risk minimization principle. Feature sparsity is then introduced by using the projected truncation technique. We derive performance bounds of the OLSF algorithm and its variants. We also conduct experiments on real-world data sets to show the performance of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

Many graph-based semi-supervised learning methods for large datasets have been proposed to cope with the rapidly increasing size of data, such as Anchor Graph Regularization (AGR). This model builds a regularization framework by exploring the underlying structure of the whole dataset with both datapoints and anchors. Nevertheless, AGR still has limitations in its two components: (1) in anchor graph construction, the estimation of the local weights between each datapoint and its neighboring anchors could be biased and relatively slow; and (2) in anchor graph regularization, the adjacency matrix that estimates the relationship between datapoints, is not sufficiently effective. In this paper, we develop an Efficient Anchor Graph Regularization (EAGR) by tackling these issues. First, we propose a fast local anchor embedding method, which reformulates the optimization of local weights and obtains an analytical solution. We show that this method better reconstructs datapoints with anchors and speeds up the optimizing process. Second, we propose a new adjacency matrix among anchors by considering the commonly linked datapoints, which leads to a more effective normalized graph Laplacian over anchors. We show that, with the novel local weight estimation and normalized graph Laplacian, EAGR is able to achieve better classification accuracy with much less computational costs. Experimental results on several publicly available datasets demonstrate the effectiveness of our approach. [ABSTRACT FROM PUBLISHER]

Mining with multilabel data is a popular topic in data mining. When performing classification on multilabel data, existing methods using traditional classifiers, such as support vector machines (SVMs), k-nearest neighbor (k-NN), and decision trees, have relatively poor accuracy and efficiency. Motivated by this, we present a new algorithm adaptation method, namely, a decision tree–based method for multilabel classification in domains with large-scale data sets called decision tree for multi-label classification (DTML). We build an incremental decision tree to reduce the learning time and divide the training data and adopt the k-NN classifier at leaves to improve the classification accuracy. Extensive studies show that our algorithm can efficiently learn from multilabel data while maintaining good performance on example-based evaluation metrics compared to nine state-of-the-art multilabel classification methods. Thus, we draw a conclusion that we provide an efficient and effective incremental algorithm adaptation method for multilabel classification especially in domains with large-scale multilabel data. [ABSTRACT FROM AUTHOR]

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. [ABSTRACT FROM AUTHOR]

An open question in ensemble-based active learning is how to choose one classifier type, or appropriate combinations of multiple classifier types, to construct ensembles for a given task. While existing approaches typically choose one classifier type, this paper presents a method that trains and adapts multiple instances of multiple classifier types toward an appropriate ensemble during active learning. The method is termed adaptive heterogeneous ensembles (henceforth referred to as AHE). Experimental evaluations show that AHE constructs heterogeneous ensembles that outperform homogeneous ensembles composed of any one of the classifier types, as well as bagging, boosting and the random subspace method with random sampling. We also show in this paper that the advantage of AHE over other methods is increased if (1) the overall size of the ensemble also adapts during learning; and (2) the target data set is composed of more than two class labels. Through analysis we show that the AHE outperforms other methods because it automatically discovers complementary classifiers: for each data instance in the data set, instances of the classifier type best suited for that data point vote together, while instances of the other, inappropriate classifier types disagree, thereby producing a correct overall majority vote. [ABSTRACT FROM AUTHOR]

In some real world applications, like information retrieval and data classification, we often are confronted with the situation that the same semantic concept can be expressed using different views with similar information. Thus, how to obtain a certain Semantically Consistent Patterns (SCP) for cross-view data, which embeds the complementary information from different views, is of great importance for those applications. However, the heterogeneity among cross-view representations brings a significant challenge on mining the SCP. In this paper, we propose a general framework to discover the SCP for cross-view data. Specifically, aiming at building a feature-isomorphic space among different views, a novel Isomorphic Relevant Redundant Transformation (IRRT) isfirst proposed. The IRRT linearly maps multiple heterogeneous low-level feature spaces to a high-dimensional redundantfeature-isomorphic one, which we name as mid-level space. Thus, much more complementary information from different views can be captured. Furthermore, to mine the semantic consistency among the isomorphic representations in the mid-level space, we propose a new Correlation-based Joint Feature Learning (CJFL) model to extract a unique high-level semantic subspace shared across the feature-isomorphic data. Consequently, the SCP for cross-view data can be obtained. Comprehensive experiments on three data sets demonstrate the advantages of our framework in classification and retrieval. [ABSTRACT FROM AUTHOR]

Mining frequent patterns with periodic wildcard gaps is a critical data mining problem to deal with complex real-world problems. This problem can be described as follows: given a subject sequence, a pre-specified threshold, and a variable gap-length with wildcards between each two consecutive letters. The task is to gain all frequent patterns with periodic wildcard gaps. State-of-the-art mining algorithms which use matrices or other linear data structures to solve the problem not only consume a large amount of memory but also run slowly. In this study, we use an Incomplete Nettree structure (the last layer of a Nettree which is an extension of a tree) of a sub-pattern P to efficiently create Incomplete Nettrees of all its super-patterns with prefix pattern P and compute the numbers of their supports in a one-way scan. We propose two new algorithms, MAPB (Mining sequentiAl Pattern using incomplete Nettree with Breadth first search) and MAPD (Mining sequentiAl Pattern using incomplete Nettree with Depth first search), to solve the problem effectively with low memory requirements. Furthermore, we design a heuristic algorithm MAPBOK (MAPB for tOp-K) based on MAPB to deal with the Top- K frequent patterns for each length. Experimental results on real-world biological data demonstrate the superiority of the proposed algorithms in running time and space consumption and also show that the pattern matching approach can be employed to mine special frequent patterns effectively. [ABSTRACT FROM AUTHOR]

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution. [ABSTRACT FROM AUTHOR]

It is a nontrivial task to build an accurate emerging pattern (EP) classifier from high-dimensional data because we inevitably face two challenges 1) how to efficiently extract a minimal set of strongly predictive EPs from an explosive number of candidate patterns, and 2) how to handle the highly sensitive choice of the minimal support threshold. To address these two challenges, we bridge causal relevance and EP discriminability (the predictive ability of emerging patterns) to facilitate EP mining and propose a new framework of mining EPs from high-dimensional data. In this framework, we study the relationships between causal relevance in a causal Bayesian network and EP discriminability in EP mining, and then reduce the pattern space of EP mining to direct causes and direct effects, or the Markov blanket (MB) of the class attribute in a causal Bayesian network. The proposed framework is instantiated by two EPs-based classifiers, CE-EP and MB-EP, where CE stands for direct Causes and direct Effects, and MB for Markov Blanket. Extensive experiments on a broad range of data sets validate the effectiveness of the CE-EP and MB-EP classifiers against other well-established methods, in terms of predictive accuracy, pattern numbers, running time, and sensitivity analysis. [ABSTRACT FROM PUBLISHER]

Most previous studies on rough sets focused on attribute reduction and decision rule mining on a single concept level. Data with attribute value taxonomies (AVTs) are, however, commonly seen in real-world applications. In this paper, we extend Pawlak’s rough set model, and propose a novel multi-level rough set model (MLRS) based on AVTs and a full-subtree generalization scheme. Paralleling with Pawlak’s rough set model, some conclusions related to the MLRS are given. Meanwhile, a novel concept of cut reduction based on MLRS is presented. A cut reduction can induce the most abstract multi-level decision table with the same classification ability on the raw decision table, and no other multi-level decision table exists that is more abstract. Furthermore, the relationships between attribute reduction in Pawlak’s rough set model and cut reduction in MLRS are discussed. We also prove that the problem of cut reduction generation is NP-hard, and develop a heuristic algorithm named CRTDR for computing the cut reduction. Finally, an approach named RMTDR for mining multi-level decision rule is provided. It can mine decision rules from different concept levels. Example analysis and comparative experiments show that the proposed methods are efficient and effective in handling the problems where data is associated with AVTs. [ABSTRACT FROM AUTHOR]

Abstract: Identity disclosure is one of the most serious privacy concerns in many data mining applications. A well-known privacy model for protecting identity disclosure is k-anonymity. The main goal of anonymizing classification data is to protect individual privacy while maintaining the utility of the data in building classification models. In this paper, we present an approach based on rough sets for measuring the data quality and guiding the process of anonymization operations. First, we make use of the attribute reduction theory of rough sets and introduce the conditional entropy to measure the classification data quality of anonymized datasets. Then, we extend conditional entropy under single-level granulation to hierarchical conditional entropy under multi-level granulation, and study its properties by dynamically coarsening and refining attribute values. Guided by these properties, we develop an efficient search metric and present a novel algorithm for achieving k-anonymity, Hierarchical Conditional Entropy-based Top-Down Refinement (HCE-TDR), which combines rough set theory and attribute value taxonomies. Theoretical analysis and experiments on real world datasets show that our algorithm is efficient and improves data utility. [Copyright &y& Elsevier]

We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. From the 10 cases we find that supervised methods prevail while unsupervised techniques play a supporting role. Further, significant domain knowledge is generally required to achieve a completed solution. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single "aha moments" of knowledge ("nugget") discovery. [ABSTRACT FROM AUTHOR]

Abstract: Nonnegative Matrix Factorization (NMF), a parts-based representation using two small factor matrices to approximate an input data matrix, has been widely used in data mining, pattern recognition and signal processing. Orthogonal NMF which imposes orthogonality constraints on the factor matrices can improve clustering performance. However, the existing orthogonal NMF algorithms are either computationally expensive or have to incorporate prior information to achieve orthogonality. In our research, we propose an algorithm called Nonnegative Matrix Factorization on Orthogonal Subspace (NMFOS), in which the generation of orthogonal factor matrices is part of objective function minimization. Thus, orthogonality is achieved without resorting to additional constraints, and the computational complexity is decreased. We develop two algorithms based on the Euclidean distance metric and the generalized Kullback–Leibler divergence, respectively. Experiments on 10 document datasets show that NMFOS improves clustering accuracy. On a facial image database, NMFOS achieves a better parts-based representation with a significant reduction in computational complexity. [Copyright &y& Elsevier]

In October 2005, we took an initiative to identify 10 challenging problems in data mining research, by consulting some of the most active researchers in data mining and machine learning for their opinions on what are considered important and worthy topics for future research in data mining. We hope their insights will inspire new research efforts, and give young researchers (including PhD students) a high-level guideline as to where the hot problems are located in data mining. Due to the limited amount of time, we were only able to send out our survey requests to the organizers of the IEEE ICDM and ACM KDD conferences, and we received an overwhelming response. We are very grateful for the contributions provided by these researchers despite their busy schedules. This short article serves to summarize the 10 most challenging problems of the 14 responses we have received from this survey. The order of the listing does not reflect their level of importance. [ABSTRACT FROM AUTHOR]

Many large organizations have multiple databases distributed in different branches, and therefore multi-database mining is an important task for data mining. To reduce the search cost in the data from all databases, we need to identify which databases are most likely relevant to a data mining application. This is referred to as database selection. For real-world applications, database selection has to be carried out multiple times to identify relevant databases that meet different applications. In particular, a mining task may be without reference to any specific application. In this paper, we present an efficient approach for classifying multiple databases based on their similarity between each other. Our approach is application-independent. [Copyright &y& Elsevier]

Dealing with very large databases is one of the defining challenges in data mining research and development. Some databases are simply too large (e.g., with terabytes of data) to be processed at one time. For efficiency and space reasons, partitioning them into subsets for processing is necessary. However, since the number of itemsets in each partitioned data subset can be a combinatorial amount and each of them may be a large itemset in the original database, data mining results from these subsets can be very large in size. Therefore, the key to data partitioning is how to aggregate the results from these subsets. It is not realistic to keep all results from each subset, because the rules from one subset need to be verified for usefulness in other subsets. This article presents a model of aggregating association rules from different data subsets by weighting. In particular, the aggregation efficiency is enhanced by rule selection. [ABSTRACT FROM AUTHOR]

Presents a heuristic, attribute-based, noise-tolerant data mining program, HCV (Version 2.0), based on a developed extension matrix approach. Methods used; Issues to be addressed when integrating machine learning techniques into database systems to build practical data mining systems; Conclusions of the study; Statistical information supporting this research.

The news filtering and summarization (NFAS) system can automatically recognize Web news pages, retrieve each news page's title and news content, and extract key phrases. This extraction method substantially outperforms methods based on term frequency and lexical chains. [ABSTRACT FROM PUBLISHER]

Abstract: Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging or aggressive classifier ensemble (ACE) , which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer. [Copyright &y& Elsevier]