Pedro González

An overview on subgroup discovery: foundations and applications

Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.

Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting information that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.

NMEEF-SD: Non-dominated Multiobjective Evolutionary Algorithm for Extracting Fuzzy Rules in Subgroup Discovery

A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is based on the hybridization between fuzzy logic and genetic algorithms, deals with subgroup-discovery problems in order to extract novel and interpretable fuzzy rules of interest, and the evolutionary fuzzy system NMEEF-SD is based on the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II) model but is oriented toward the subgroup-discovery task using specific operators to promote the extraction of interpretable and high-quality subgroup-discovery rules. The proposal includes different mechanisms to improve diversity in the population and permits the use of different combinations of quality measures in the evolutionary process. An elaborate experimental study, which was reinforced by the use of nonparametric tests, was performed to verify the validity of the proposal, and the proposal was compared with other subgroup discovery methods. The results show that NMEEF-SD obtains the best results among several algorithms studied.

On the discovery of association rules by means of evolutionary algorithms

Association rule learning is a data mining task that tries to discover interesting relations between variables in large databases. A review of association rule learning is presented that focuses on the use of evolutionary algorithms not only applied to Boolean variables but also to categorical and quantitative ones. The use of fuzzy rules in the evolutionary algorithms for association rule learning is also described. Finally, the main applications of association rule evolutionary learning covered by the specialized bibliography are reviewed.

Multiobjective evolutionary induction of subgroup discovery fuzzy rules: a case study in marketing

This paper presents a multiobjective genetic algorithm which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The evolutionary algorithm follows a multiobjective approach in order to optimize in a suitable way the different quality measures used in this kind of problems. Experimental evaluation of the algorithm, applying it to a market problem studied in the University of Mondragon (Spain), shows the validity of the proposal. The application of the proposal to this problem allows us to obtain novel and valuable knowledge for the experts.

Evolutionary fuzzy rule extraction for subgroup discovery in a psychiatric emergency department

This paper describes the application of evolutionary fuzzy systems for subgroup discovery to a medical problem, the study on the type of patients who tend to visit the psychiatric emergency department in a given period of time of the day. In this problem, the objective is to characterise subgroups of patients according to their time of arrival at the emergency department. To solve this problem, several subgroup discovery algorithms have been applied to determine which of them obtains better results. The multiobjective evolutionary algorithm MESDIF for the extraction of fuzzy rules obtains better results and so it has been used to extract interesting information regarding the rate of admission to the psychiatric emergency department.

Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning. In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will focus on the suitability and potential of the search performed by EAs in the development of SD algorithms. Future directions in the use of EAs for SD are also presented in order to show the advantages and benefits that this search strategy contribute to this task.

Evolutionary algorithms for subgroup discovery applied to e-learning data

This work presents the application of subgroup discovery techniques to e-learning data from learning management systems (LMS) of andalusian universities. The objective is to extract rules describing relationships between the use of the different activities and modules available in the e-learning platform and the final mark obtained by the students. For this purpose, the results of different classical and evolutionary subgroup discovery algorithms are compared, showing the adequacy of the evolutionary algorithms to solve this problem. Some of the rules obtained are analyzed with the aim of extract knowledge allowing the teachers to take actions to improve the performance of their students.

MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology

Subgroup discovery is a broadly applicable data mining technique whose main objective is the search for descriptions of subgroups of data that are statistically unusual with respect to a property of interest. The obtaining of general rules describing as many instances as possible is preferred in subgroup discovery, but this can lead to less accurate descriptions that incorrectly describe some instances. Under certain conditions, these incorrectly-described instances can be grouped into exceptions. A new post-processing methodology for the detection of exceptions associated to previously discovered subgroups is presented in this paper. The purpose is to obtain a new description to improve the accuracy of the initial subgroup and to describe new small spaces in data with unusual behaviour within the subgroup. This post-processing methodology can be applied to the results of any subgroup discovery algorithm. A post-processing multiobjective evolutionary fuzzy system is developed following this methodology, the Multiobjective Evolutionary Fuzzy system for the detection of Exceptions in Subgroups (MEFES). A wide experimental study has been performed, supported by statistical tests, comparing the results obtained by representative subgroup discovery algorithms with those obtained after applying the post-processing algorithm. Finally, MEFES is applied in a real problem related to the description of the behaviour of a type of solar cell in the Concentrating Photovoltaic area providing useful information to the experts.

Non-dominated Multi-objective Evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery

A new multi-objective evolutionary model for subgroup discovery with fuzzy rules is presented in this paper. The method resolves subgroup discovery problems based on the hybridization between fuzzy logic and genetic algorithms, with the aim of extracting interesting, novel and interpretable fuzzy rules. To do so, the algorithm includes different mechanisms for improving diversity in the population. This proposal focuses on the classification of individuals in fronts, based on non-dominated sort. A study can be seen for the proposal and other previous methods for different databases. In this study good results are obtained for subgroup discovery by this new evolutionary model in comparison with existing algorithms.