José Fco. Martínez-Trinidad

Mining frequent patterns and association rules using similarities

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.

OClustR: A new graph-based algorithm for overlapping clustering

Clustering is a Data Mining technique, which has been widely used in many practical applications. From these applications, there are some, like social network analysis, topic detection and tracking, information retrieval, categorization of digital libraries, among others, where objects may belong to more than one cluster; however, most clustering algorithms build disjoint clusters. In this work, we introduce OClustR, a new graph-based clustering algorithm for building overlapping clusters. The proposed algorithm introduces a new graph-covering strategy and a new filtering strategy, which together allow to build overlapping clusterings more accurately than those built by previous algorithms. The experimental evaluation, conducted over several standard collections, showed that our proposed algorithm builds less clusters than those built by the previous related algorithms. Additionally, OClustR builds clusters with overlapping closer to the real overlapping in the collections than the overlapping generated by other clustering algorithms.

A survey of emerging patterns for supervised classification

Obtaining accurate class prediction of a query object is an important component of supervised classification. However, it could be also important to understand the classification in terms of the application domain, mostly if the prediction disagrees with the expected results. Many accurate classifiers are unable to explain their classification results in terms understandable by an application expert. Classifiers based on emerging patterns, on the other hand, are accurate and easy to understand. The goal of this article is to review the state-of-the-art methods for mining emerging patterns, classify them by different taxonomies, and identify new trends. In this survey, we present the most important emerging pattern miners, categorizing them on the basis of the mining paradigm, the use of discretization, and the stage where the mining occurs. We provide detailed descriptions of the mining paradigms with their pros and cons, what helps researchers and users to select the appropriate algorithm for a given application.

Immediate water quality assessment in shrimp culture using fuzzy inference systems

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.

Fuzzy emerging patterns for classifying hard domains

Emerging pattern–based classification is an ongoing branch in Pattern Recognition. However, despite its simplicity and accurate results, this classification includes an a priori discretization step that may degrade the classification accuracy. In this paper, we introduce fuzzy emerging patterns as an extension of emerging patterns to deal with numerical attributes using fuzzy discretization. Based on fuzzy emerging patterns, we propose a new classifier that uses a novel graph organization of patterns. The new classifier outperforms some popular and state of the art classifiers on several UCI repository databases. In a pairwise comparison, it significantly beats every other single classifier.

LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification

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.

AGraP: an algorithm for mining frequent patterns in a single graph using inexact matching

Frequent graph mining algorithms commonly use graph isomorphism to identify occurrences of a given pattern, but in the last years, a few works have focused on the case where a pattern could differ from its occurrences, which can be important to analyze noisy data. These later algorithms allow differences in labels and structural differences in edges, but to the best of our knowledge, none of them considers structural differences in vertices. How can we identify occurrences that differ by one (or several) nodes from the pattern they represent? Our work approaches the problem of frequent graph pattern mining with two main characteristics. First, we use inexact matching, allowing structural differences in both edges and vertices. Second, we focus on the problem of mining patterns in a single graph, a problem that has been less explored than the case in which patterns are mined from a graph collection. In this paper, we introduce two similarity functions to compare graphs using inexact matching and an algorithm, AGraP, able to identify patterns that can have structural differences with respect to their occurrences. Our experimental results show that AGraP is able to find patterns that cannot be found by other state-of-the-art algorithms. Additionally, we show that the patterns mined by AGraP are useful in classification tasks.

A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text

One of the sequential pattern mining problems is to find the maximal frequent sequences in a database with a β support. In this paper, we propose a new algorithm to find all the maximal frequent sequences in a text instead of a database. Our algorithm in comparison with the typical sequential pattern mining algorithms avoids the joining, pruning and text scanning steps. Some experiments have shown that it is possible to get all the maximal frequent sequences in a few seconds for medium texts.

Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases

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. HighlightsThe effect of resampling methods on contrast pattern based classifiers.A study of contrast pattern based classifiers in class imbalance problems.We show how improving the performance of contrast pattern based classifiers.We provide a rough guide for selecting the best resampling method regarding the IR.

Mining frequent connected subgraphs reducing the number of candidates

In this paper, a new algorithm for mining frequent connected subgraphs called gRed ( g raph Candidate Red uction Miner) is presented. This algorithm is based on the gSpan algorithm proposed by Yan and Jan. In this method, the mining process is optimized introducing new heuristics to reduce the number of candidates. The performance of gRed is compared against two of the most popular and efficient algorithms available in the literature (gSpan and Gaston). The experimentation on real world databases shows the performance of our proposal overcoming gSpan, and achieving better performance than Gaston for low minimal support when databases are large.