Jesús Ariel Carrasco-Ochoa

Classifying Using Specific Rules with High Confidence

In this paper, we introduce a new strategy for mining the set of Class Association Rules (CARs), that allows building specific rules with high confidence. Moreover, we introduce two propositions that support the use of a confidence threshold value equal to

General framework for class-specific feature selection

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Mining frequent patterns and association rules using similarities

. We also propose a new way for ordering the set of CARs based on rule size and confidence values. Our results show a better average classification accuracy than those obtained by the best classifiers based on CARs reported in the literature.

OClustR: A new graph-based algorithm for overlapping clustering

Commonly, when a feature selection algorithm is applied, a single feature subset is selected for all the classes, but this subset could be inadequate for some classes. Class-specific feature selection allows selecting a possible different feature subset for each class. However, all the class-specific feature selection algorithms have been proposed for a particular classifier, which reduce their applicability. In this paper, a general framework for using any traditional feature selector for doing class-specific feature selection, which allows using any classifier, is proposed. Experimental results and a comparison against traditional feature selectors showing the suitability of the proposed framework are included.

A survey of emerging patterns for supervised classification

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.

Immediate water quality assessment in shrimp culture using fuzzy inference systems

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.

Document clustering based on maximal frequent sequences

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.

Fuzzy emerging patterns for classifying hard domains

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.

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

Document clustering has the goal of discovering groups with similar documents. The success of the document clustering algorithms depends on the model used for representing these documents. Documents are commonly represented with the vector space model based on words or n-grams. However, these representations have some disadvantages such as high dimensionality and loss of the word sequential order. In this work, we propose a new document representation in which the maximal frequent sequences of words are used as features of the vector space model. The proposed model efficiency is evaluated by clustering different document collections and compared against the vector space model based on words and n-grams, through internal and external measures.

Mining patterns for clustering on numerical datasets using unsupervised decision trees

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.