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In rough set theory, a construct is an attribute subset with the same ability to discern objects belonging to different classes as the whole set of attributes, while maintaining the similarity between objects belonging to the same class. Although algorithms for reducts computation can be adapted to compute constructs, practical problems exist where these algorithms cannot compute all constructs within a reasonable time frame. Therefore, this paper introduces an algorithm for computing all constructs of a decision system. The results of experiments with various decision systems (both artificial and real-world) suggest that our algorithm is, in most cases, faster than the state-of-the-art algorithms when the simplified binary discernibility–similarity matrix has a density of less than 0.29. [ABSTRACT FROM AUTHOR]

• A supervised filter feature selection method for mixed data is introduced • Spectral analysis and Information-theory based feature selection was applied • Relevant and non-redundant features are selected • An evaluation of the proposed method on 40 public datasets was performed • A comprehensive comparison against state-of-the-art methods was made Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusively numerical or non- numerical data. In this paper, we propose a supervised filter feature selection method that combines Spectral Feature Selection and Information-theory based redundancy analysis for selecting relevant and non-redundant features in supervised mixed datasets; i.e., datasets where the objects are described simultaneously by both, numerical and non-numerical features. To demonstrate the effectiveness of our proposed supervised filter feature selection method, we conducted several experiments on 40 public real-world datasets. Additionally, we compare our method against relevant state-of-the-art supervised filter methods for numerical, non-numerical, and mixed data. From this comparison, our method, in general, obtains better results than the results obtained by the other evaluated filter feature selection methods. [ABSTRACT FROM AUTHOR]

• A new algorithm for computing all the shortest reducts of a decision system is introduced. • Some of the most effective pruning properties for computing all reducts are adapted to compute only the shortest reducts. • Our algorithm uses binary cumulative operations and a fast candidate evaluation process unlike other algorithms. • Our proposal is the fastest in decision systems whose simplified binary discernibility matrices have medium or high density. This paper deals with the problem of computing the shortest reducts of a decision system. The shortest reducts are useful for attribute reduction in classification problems and data size reduction. Unfortunately, finding all the shortest reducts is an NP-hard problem. There are some algorithms reported in the literature to overcome the complexity of computing the shortest reducts. However, most of these algorithms relay on costly operations for candidate evaluation. In this paper, we propose a new algorithm for computing all the shortest reducts; based on binary cumulative operations over a pair-wise comparison matrix, and a fast candidate evaluation process. Binary cumulative operations save computation time by avoiding repetitive calculations. Furthermore, unlike other algorithms reported in the literature, our candidate evaluation process relays on low-cost operations which reduce the runtime in most cases. Our experiments over synthetic and real-world decision systems show that our proposal is faster than state of the art algorithms in most decision systems. [ABSTRACT FROM AUTHOR]

Most of the current unsupervised feature selection methods are designed to process only numerical datasets. Therefore, in practical problems, where the objects under study are described through both numerical and non-numerical features (mixed datasets), these methods cannot be directly applied. In this work, we propose a new unsupervised filter feature selection method that can be used on datasets with both numerical and non-numerical features. The proposed method is inspired by the spectral feature selection, by using together a kernel and a new spectrum based feature evaluation measure for quantifying the feature relevance. Experiments on synthetic datasets show that in the 99% of the cases where the relevant features are known our method identifies and ranks the most relevant features at the beginning of a sorted list. Additionally, we contrast our method against state-of-the-art unsupervised filter methods over real datasets, and our method in most cases significantly outperforms them. [ABSTRACT FROM AUTHOR]

The representation of hieroglyphs in retrieval systems represents a great challenge since these systems' results highly depend on the used representation. In the literature, the most successful works in this area compute local descriptors from a set of points of interest taken from the entire hieroglyph and use them under the BoVW model to represent hieroglyphs. Unlike these works, this paper presents a way to extract segments from hieroglyphs and, by encoding the extracted segments through local descriptors, proposes to use them under the Bag of Visual Words (BoVW) model to represent hieroglyphs. Our experiments show that the proposed representation allows us to obtain retrieval results that overcome those reported by using state of the art representations. • Hieroglyph representation for retrieval based on encoding hieroglyph segments. • Good quality hieroglyph retrieval following the Bag of Visual Words Model. • Automatic decomposition of hieroglyphs in segments. [ABSTRACT FROM AUTHOR]

The class imbalance problem is a challenge in supervised classification, since many classifiers are sensitive to class distribution, biasing their prediction towards the majority class. Usually, in imbalanced databases, contrast pattern miners extract a very large collection of patterns from the majority class but only a few patterns (or none) from the minority class. It causes that minority class objects have low support and they could be identified as noise and consequently discarded by the contrast pattern based classifier biasing the results towards the majority class. In the literature, the class imbalance problem is commonly faced by applying resampling methods. Therefore, in this paper, we present a study about the impact of using resampling methods for improving the performance of contrast pattern based classifiers in class imbalance problems. Experimental results using standard imbalanced databases show that there are statistically significant differences between using the classifier before and after applying resampling methods. Moreover, from this study, we provide a guide based on the class imbalance ratio for selecting a resampling method that jointly with a contrast pattern based classifier allows us to have good results in a class imbalance problem. [ABSTRACT FROM AUTHOR]

Most understandable classifiers are based on contrast patterns, which can be accurately mined from decision trees. Nevertheless, tree diversity must be ensured to mine a representative pattern collection. In this paper, we performed an experimental comparison among different diversity generation procedures. We compare diversity generated by each procedure based on the amount of total, unique, and minimal patterns extracted from the induced tree for different minimal support thresholds. This comparison, together with an accuracy and abstention experiment, shows that Random Forest and Bagging generate the most diverse and accurate pattern collection. Additionally, we study the influence of data type in the results, finding that Random Forest is best for categorical data and Bagging for numerical data. Comparison includes most known diversity generation procedures and three new deterministic procedures introduced here. These deterministic procedures outperform existing deterministic method, but are still outperformed by random procedures. [ABSTRACT FROM AUTHOR]

Abstract: Most of the current algorithms for mining association rules assume that two object subdescriptions are similar when they are exactly equal, but in many real world problems some other similarity functions are used. Commonly these algorithms are divided in two steps: Frequent pattern mining and generation of interesting association rules from frequent patterns. In this work, two algorithms for mining frequent similar patterns using similarity functions different from the equality are proposed. Additionally, the GenRules Algorithm is adapted to generate interesting association rules from frequent similar patterns. Experimental results show that our algorithms are more effective and obtain better quality patterns than the existing ones. [Copyright &y& Elsevier]

Abstract: Most clustering algorithms organize a collection of objects into a set of disjoint clusters. Although this approach has been successfully applied in unsupervised learning, there are several applications where objects could belong to more than one cluster. Overlapping clustering is an alternative in those contexts like social network analysis, information retrieval and bioinformatics, among other problems where non-disjoint clusters appear. In addition, there are environments where the collection changes frequently and the clustering must be updated; however, most of the existing overlapping clustering algorithms are not able to efficiently update the clustering. In this paper, we introduce a new overlapping clustering algorithm, called DClustR, which is based on the graph theory approach and it introduces a new strategy for building more accurate overlapping clusters than those built by state-of-the-art algorithms. Moreover, our algorithm introduces a new strategy for efficiently updating the clustering when the collection changes. The experimentation conducted over several standard collections shows the good performance of the proposed algorithm, wrt. accuracy and efficiency. [Copyright &y& Elsevier]

Clustering is a Data Mining technique which has been widely used in many practical applications. In some of these applications like, medical diagnosis, categorization of digital libraries, topic detection and others, the objects could belong to more than one cluster. However, most of the clustering algorithms generate disjoint clusters. Moreover, processing additions, deletions and modifications of objects in the clustering built so far, without having to rebuild the clustering from the beginning is an issue that has been little studied. In this paper, we introduce DCS, a clustering algorithm which includes a new graph-cover strategy for building a set of clusters that could overlap, and a strategy for dynamically updating the clustering, managing multiple additions and/or deletions of objects. The experimental evaluation conducted over different collections demonstrates the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce an efficient algorithm for mining discriminative regularities on databases with mixed and incomplete data. Unlike previous methods, our algorithm does not apply an a priori discretization on numerical features; it extracts regularities from a set of diverse decision trees, induced with a special procedure. Experimental results show that a classifier based on the regularities obtained by our algorithm attains higher classification accuracy, using fewer discriminative regularities than those obtained by previous pattern-based classifiers. Additionally, we show that our classifier is competitive with traditional and state-of-the-art classifiers. [Copyright &y& Elsevier]

In recent years, Unsupervised Feature Selection (UFS) methods have attracted considerable interest in different research areas due to their wide application in problems where unlabeled data appears. Nevertheless, few UFS methods can process datasets described by both numerical and non-numerical features (mixed data). This paper introduces a new filter UFS method based on a new feature correlation measure for mixed data to select a relevant and non-redundant feature subset. The proposed method addresses the feature selection problem in two stages through a strategy that combines Spectral Feature Selection to identify relevant features and a Pair-wise Redundancy Analysis to remove those features with a high correlation with others. According to our experiments, the proposed feature selection method based on the introduced feature correlation measure provides better quality results than previous filter UFS methods for mixed data reported in the literature. • A filter Unsupervised Feature Selection method for mixed data is presented. • A new feature correlation measure for mixed data is introduced. • Spectral Feature Selection and Pair-wise Redundancy Analysis were applied. • A comprehensive evaluation of the proposed method is provided. [ABSTRACT FROM AUTHOR]

Overlapping clustering algorithms have shown to be effective for clustering documents. However, the current overlapping document clustering algorithms produce a big number of clusters, which make them little useful for the user. Therefore, in this paper, we propose a k-means based method for overlapping document clustering, which allows to specify by the user the number of groups to be built. Our experiments with different corpora show that our proposal allows obtaining better results in terms of FBcubed than other recent works for overlapping document clustering reported in the literature. [ABSTRACT FROM AUTHOR]

In supervised classification if one of the classes has fewer objects than the other, we have a class imbalance problem. One of the most common solutions to address class imbalance problems is oversampling, and SMOTE is the most referenced and well-known oversampling method. However, SMOTE creates synthetic objects in a random way, therefore it produces a different result each time it is applied, and in practice the user has to apply SMOTE several times for choosing the best of all the generated balanced datasets. For this reason, in this paper, we present SMOTE-D, a deterministic version of SMOTE, and propose new deterministic SMOTE-D-based versions of some of the most recent and successful SMOTE-based methods. In our experiments, we show that all proposed deterministic methods produce as good results as random methods but our proposals need to be applied just once. This is very important from a practical point of view since our proposals save time by avoiding multiple applications of them as SMOTE does and they provide one unique result. [ABSTRACT FROM AUTHOR]

Most algorithms to mine graph patterns, during the searching process, require a pattern to be identical to its occurrences, relying on the graph isomorphism problem. However, in recent years, there has been interest in the case in which it is acceptable to have some differences between a pattern and its occurrences, whether these differences are in labels or in structure. Allowing some differences and using inexact matching to measure the similarity between graphs lead to the discovery of new patterns, but some important challenges, such as the increment on the number of found patterns, make the post-mining analysis harder. In this work we focus on two extensions of the AGraP algorithm, which mines inexact patterns, addressing the issue of reducing the output pattern set while trying to retain the useful information gained through the use of inexact matching. First, exploring a traditional approach, we propose the CloseAFG algorithm that focuses on closed patterns. Then, we propose the IntAFG algorithm to find a subset of patterns covering the original pattern set, while lessening redundancy among selected patterns. We show the performance of our approaches through some experiments on synthetic databases; additionally, we also show the usefulness of the reduced pattern sets for image classification. [ABSTRACT FROM AUTHOR]

Contrast patterns, which lie in the core of most understandable classifiers, are frequently evaluated by quality measures. Since many different quality measures are available, they should be compared to select the most appropriate for each applications. This paper introduces a method to compare quality measures, using a set of mined patterns and a collection of objects not used for mining. The comparison is performed by correlating quality values with a quality estimation of the patterns. Additionally, a meta-learning study is performed to show that combining quality measures could be better than using the best single measures in isolation. The results of this paper can help researchers to create new quality measures or to find new combinations of quality measures to create better understandable classification systems. [ABSTRACT FROM AUTHOR]

Contrast pattern-based classifiers are an important family of both understandable and accurate classifiers. Nevertheless, these classifiers do not achieve good performance on class imbalance problems. In this paper, we introduce a new contrast pattern-based classifier for class imbalance problems. Our proposal for solving the class imbalance problem combines the support of the patterns with the class imbalance level at the classification stage of the classifier. From our experimental results, using highly imbalanced databases, we can conclude that our proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems. Additionally, we show that our classifier significantly outperforms other state-of-the-art classifiers not directly based on contrast patterns, which are also designed to deal with class imbalance problems. [ABSTRACT FROM AUTHOR]

Feature selection is a common task in areas such as Pattern Recognition, Data Mining, and Machine Learning since it can help to improve prediction quality, reduce computation time and build more understandable models. Although feature selection for supervised classification has been widely studied, feature selection in the absence of class labels, namely feature selection for clustering or unsupervised feature selection, has been less addressed. Most existing unsupervised feature selection approaches suffer from the called “Bias of Criterion Values to Dimension,” which arises when feature subsets with different cardinality are evaluated by an internal evaluation clustering criterion. In this paper, we introduce a new hybrid filter–wrapper method for clustering, which combines the spectral feature selection framework using the Laplacian Score ranking and a modified Calinski–Harabasz index. The proposed method in the filter stage sorts the features according to their relevance, while in the wrapper stage, through our modified Calinski–Harabasz index that takes into account the cardinality of the feature subsets under evaluation, evaluates the features considering them as a subset rather than individually by using two well-known selection strategies. Experiments on different datasets show that the proposed method alleviates the “Bias of Criterion Values to Dimension” and, identifies and selects more relevant features than those selected by other reported hybrid filter–wrapper feature selection methods for clustering. Additionally, we also contrast our results against other filter and wrapper methods of the state-of-the-art. [ABSTRACT FROM AUTHOR]

In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel "Dynamic K" satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the "Dynamic K" satisfaction mechanism allow improving the performance of CAR-based classifiers. [ABSTRACT FROM AUTHOR]

Measuring the quality of a contrast pattern is an active and relevant area of pattern recognition and data mining. Quality measures are important tools in very different scenarios like supervised classification, pattern based clustering, and association rule mining. Consequently, and due to the large collection of available measures, it is important to perform comparative studies for each particular context. Most published studies comparing quality measures are theoretical and in the context of association rule evaluation. In this paper, we present an empirical comparison of the behavior of 33 quality measures in the context of supervised classification and contrast pattern filtering. A comprehensive experimentation using several databases compares the behavior of these measures in three different contexts: as aggregation value, as pattern evaluation for classification, and as pattern evaluation for filtering. Experiments also show that top-accurate quality measures for classification have a deceptive performance for pattern filtering, because they cannot distinguish among patterns with zero support in the negative class. [ABSTRACT FROM AUTHOR]

Abstract: Association rule mining and classification are important tasks in data mining. Using association rules has proved to be a good approach for classification. In this paper, we propose an accurate classifier based on class association rules (CARs), called CAR-IC, which introduces a new pruning strategy for mining CARs, which allows building specific rules with high confidence. Moreover, we propose and prove three propositions that support the use of a confidence threshold for computing rules that avoids ambiguity at the classification stage. This paper also presents a new way for ordering the set of CARs based on rule size and confidence. Finally, we define a new coverage strategy, which reduces the number of non-covered unseen-transactions during the classification stage. Results over several datasets show that CAR-IC beats the best classifiers based on CARs reported in the literature. [Copyright &y& Elsevier]

In this paper, an accurate classifier based on Class Association Rules (CARs), called CAR-NF, is proposed. CAR-NF introduces a new strategy for computing CARs, using the Netconf as measure of interest, that allows to prune the CAR search space for building specific rules with high Netconf. Moreover, we propose and prove a proposition that supports the use of a Netconf threshold value equal to 0.5 for mining the CARs. Additionally, a new way for ordering the set of CARs based on their rule sizes and Netconf values is introduced in CAR-NF. The ordering strategy together with the "Best K rules" satisfaction mechanism allows CAR-NF to have better accuracy than CBA, CMAR, CPAR, TFPC and HARMONY classifiers, the best classifiers based on CARs reported in the literature. [ABSTRACT FROM AUTHOR]

• A systematic evaluation of filter Unsupervised Feature Selection methods is presented. • The most popular and recent filter UFS methods are included in our study. • The evaluation of the filter UFS methods followed the standards in the literature. • A general discussion based on the results of the evaluated methods is provided. • Some guidelines for the use of the evaluated filter UFS methods is also provided. Unsupervised Feature Selection (UFS) has aroused great interest in the last years because of its practical significance and application on a large variety of problems in expert and intelligent systems where unlabeled data appear. Specifically, Unsupervised Feature Selection methods based on the filter approach have received more attention due to their efficiency, scalability, and simplicity. However, in the literature, there are no comprehensive studies for assessing such UFS methods when they are applied, under the same conditions, to a wide variety of real-world data. To fill this gap, in this paper, we present a comprehensive empirical and systematic evaluation of the most popular and recent filter UFS methods, evaluating their performance in terms of clustering, classification, and runtime. The filter methods used in our study were applied on 50 datasets from the UCI Machine Learning Repository and 25 high dimensional datasets from the ASU Feature Selection Repository. To evaluate if the outcomes obtained by the assessed methods are statistically significant, the Friedman test and Holm post hoc procedure were applied in the clustering and classification results. From our experiments, we provide some practical guidelines and insights for the use of the filter UFS methods analyzed in our study. [ABSTRACT FROM AUTHOR]

Highlights • Outlier detection is important for improving clustering results. • Over detection of outliers leads to information loss. • Our proposal reduces the number of over-detected outliers. • Experiments show that clustering quality can be improved, while runtime is reduced. Abstract Expert and intelligent systems use a variety of machine learning techniques to obtain and understand the information inherent in the data. Clustering is one of these techniques, which has become important and popular since it allows classifying an unlabeled dataset into clusters of similar objects. There are many clustering algorithms that have been proposed in the literature. From these algorithms, the Cross-Clustering algorithm is one of the most recent clustering algorithms for partial clustering (clustering where not necessarily all the objects are grouped into clusters), which has provided good results allowing estimating a suitable set of clusters, as well as eliminating outliers. However, this algorithm tends to eliminate too many objects as outliers, which leads to discard a lot of non-outlier objects. Additionally, the Cross-Clustering algorithms spends a lot of time evaluating several combinations of clusterings, trying to determine a suitable number of clusters. To overcome these problems, in this paper, an improved version of the Cross-Clustering algorithm (ICC) is proposed. ICC changes the clustering algorithm used for detecting outliers, as well as it modifies the way outliers are detected. Moreover, a stop criterion allowing to make a fast decision on the estimation of a suitable number of cluster, is also introduced. The performance of the improved Cross-Clustering algorithm is compared with the original algorithm on artificial and real datasets. Our results show that ICC improves the original algorithm and other state of the art clustering algorithms; in both, runtime and clustering quality. [ABSTRACT FROM AUTHOR]

Feature selection is a crucial aspect in classification problems, especially in domains such as text classification, where usually there is a large number of features. Recently, a two-stage feature selection method for text classification which combines class-based and corpus-based feature selection, was introduced. Based on their experiments, the authors conclude what parameter values for both, corpus-based and class-based approaches, allow a feature selection which improves the traditional methods in text classification. In this paper, we revisit this two-stage feature selection method and based on several experiments we come to a different conclusion: the parameters suggested by the original work do not necessarily provide the best results. Based on our experiments, we conclude that by combining the best parameter value for each stage, for the specific corpus under study, the two stage selection method based on coverage policies provides a subset of features which allows to get statistically significant increase over the traditional methods in the success rates of the classifier. [ABSTRACT FROM AUTHOR]

Frequent pattern mining is considered a key task to discover useful information. Despite the quality of solutions given by frequent pattern mining algorithms, most of them face the challenge of how to reduce the number of frequent patterns without information loss. Frequent itemset mining addresses this problem by discovering a reduced set of frequent itemsets, named closed frequent itemsets , from which the entire frequent pattern set can be recovered. However, for frequent similar pattern mining , where the number of patterns is even larger than for Frequent itemset mining, this problem has not been addressed yet. In this paper, we introduce the concept of closed frequent similar pattern mining to discover a reduced set of frequent similar patterns without information loss. Additionally, a novel closed frequent similar pattern mining algorithm, named CFSP-Miner , is proposed. The algorithm discovers frequent patterns by traversing a tree that contains all the closed frequent similar patterns. To do this efficiently, several lemmas to prune the search space are introduced and proven. The results show that CFSP-Miner is more efficient than the state-of-the-art frequent similar pattern mining algorithms, except in cases where the number of frequent similar patterns and closed frequent similar patterns are almost equal. However, CFSP-Miner is able to find the closed similar patterns, yielding a reduced size of the discovered frequent similar pattern set without information loss. Also, CFSP-Miner shows good scalability while maintaining an acceptable runtime performance. [ABSTRACT FROM AUTHOR]

Into the data mining field, frequent approximate subgraph (FAS) mining has become an important technique with a broad spectrum of real-life applications. This fact is because several real-life phenomena can be modeled by graphs. In the literature, several algorithms have been reported for mining frequent approximate patterns on simple-graph collections; however, there are applications where more complex data structures, as multi-graphs, are needed for modeling the problem. But to the best of our knowledge, there is no FAS mining algorithm designed for dealing with multi-graphs. Therefore, in this paper, a canonical form (CF) for simple-graphs is extended to allow representing multi-graphs and a state-of-the-art algorithm for FAS mining is also extended for processing multi-graph collections by using the extended CF. Our experiments over different synthetic and real-world multi-graph collections show that the proposed algorithm has a good performance in terms of runtime and scalability. Additionally, we show the usefulness of the patterns computed by our algorithm in an image classification problem where images are represented as multi-graphs. [ABSTRACT FROM AUTHOR]

• Fast oversampling method in the string space based on the edit distance. • Good quality oversampling not based on searching nearest neighbors. • Hierarchical synthetic object generation for class imbalance problems. In syntactic and structural pattern recognition, data represented as strings appear in several supervised classification applications. In some situations, data collections show imbalanced class distributions, which typically results in the classifier biasing its performance to the class representing the majority of objects. To solve this problem, some oversampling methods have been proposed for data represented as strings. However, this type of method has been little studied in the literature. Therefore, in this paper, we present an oversampling method for working in string space that balances the minority class and gets better classification results than state-of-the-art oversampling methods, especially for highly imbalanced problems. Furthermore, according to our experiments, the proposed method is much faster than those reported in the literature. [ABSTRACT FROM AUTHOR]

Abstract: Water quality assessment is an important activity for controlling harmful crisis in aquaculture systems. The objective of our study was to develop a new Water Quality Index focused on monitoring of shrimp farms; detecting poor water quality and preventing negative effects in the ecosystem. Usually, several water quality parameters are monitored and measured in a shrimp farm during a farming period. Those parameters are classified according to their negative effects in the ecosystem and their respective allowed limits are also defined. The proposed Water Quality Index assigns a priority level to each water parameter through a new analytical hierarchical process (AHP), which allows an accurate assessment of the water quality. Our proposed index was applied to assess the water quality condition in extensive shrimp farms in Mexico. A comparison between our approach and those proposed in the literature shows its good performance when real environments are assessed. [Copyright &y& Elsevier]

Abstract: In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas. [Copyright &y& Elsevier]

Abstract: The continuous monitoring of physical, chemical and biological parameters in shrimp culture is an important activity for detecting potential crisis that can be harmful for the organisms. Water quality can be assessed through toxicological tests evaluated directly from water quality parameters involved in the ecosystem; these tests provide an indicator about the water quality. The aim of this study is to develop a fuzzy inference system based on a reasoning process, which involves aquaculture criteria established by official organizations and researchers for assessing water quality by analyzing the main factors that affect a shrimp ecosystem. We propose to organize the water quality parameters in groups according to their importance; these groups are defined as daily, weekly and by request monitoring. Additionally, we introduce an analytic hierarchy process to define priorities for more critical water quality parameters and groups. The proposed system analyzes the most important parameters in shrimp culture, detects potential negative situations and provides a new water quality index (WQI), which describes the general status of the water quality as excellent, good, regular and poor. The Canadian water quality and other well-known hydrological indices are used to compare the water quality parameters of the shrimp water farm. Results show that WQI index has a better performance than other indices giving a more accurate assessment because the proposed fuzzy inference system integrates all environmental behaviors giving as result a complete score. This fuzzy inference system emerges as an appropriated tool for assessing site performance, providing assistance to improve production through contingency actions in polluted ponds. [Copyright &y& Elsevier]

Support calculation and duplicate detection are the most challenging and unavoidable subtasks in frequent connected subgraph (FCS) mining. The most successful FCS mining algorithms have focused on optimizing these subtasks since the existing solutions for both subtasks have high computational complexity. In this paper, we propose two novel properties that allow removing all duplicate candidates before support calculation. Besides, we introduce a fast support calculation strategy based on embedding structures. Both properties and the new embedding structure are used for designing two new algorithms: gdFil for mining all FCSs; and gdClosed for mining all closed FCSs. The experimental results show that our proposed algorithms get the best performance in comparison with other well known algorithms. [ABSTRACT FROM AUTHOR]

In this paper, two algorithms for mining frequent itemsets in large sparse datasets are proposed. The first one, named Compressed Arrays (CA), allows to process datasets that do not change along the time (static datasets) while the second one, based on the ideas of the former and named Dynamic Compressed Arrays (DCA), processes datasets that change along the time by adding/deleting transactions (dynamic datasets). Both algorithms introduce a novel way to use equivalence classes of itemsets by performing a breadth first search through them and by storing the class prefix support in compressed arrays, which allows fast itemset support computing. On the other hand, unlike previous algorithms for dynamic datasets that store the full dataset in main memory without reusing the current frequent itemsets, DCA algorithm stores the current frequent itemsets in binary files, grouped in equivalence classes, and reuses them to calculate the new frequent itemsets. [ABSTRACT FROM AUTHOR]

Most of the Frequent Connected Subgraph Mining (FCSM) algorithms have been focused on detecting duplicate candidates using canonical form (CF) tests. CF tests have high computational complexity, which affects the efficiency of graph miners. In this paper, we introduce novel properties of the canonical adjacency matrices for reducing the number of CF tests in FCSM. Based on these properties, a new algorithm for frequent connected subgraph mining called grCAM is proposed. The experiments on real world datasets show the impact of the proposed properties in FCSM. Besides, the performance of our algorithm is compared against some other reported algorithms. [ABSTRACT FROM AUTHOR]