Pedro González

Universidad de Jaén | UJAEN · Department of Computer Sciences

Today, the number of existing devices generates immense amounts of data on a continuous basis that must be processed by new distributed data stream mining approaches. In this paper we present a new approach for extracting descriptive emerging patterns in massive data streams from different sources through Apache Kafka and Apache Spark Streaming who...

Streaming is being increasingly demanded because it helps in analyzing data in real-time and in decision making. Over time, the number of existing devices increases continuously, generating a huge amount of data. Processing this data with traditional algorithms is impractical, so it is necessary to apply distributed algorithms in a Big Data context...

A preliminary many objective algorithm for extracting fuzzy emerging patterns is presented in this contribution. The proposed algorithm employs fuzzy logic together with an evolutionary algorithm. The aim is to expand the complex search space that we have in emerging pattern mining.

In this paper, a cooperative-competitive multi-objective evolutionary fuzzy system called E2PAMEA is presented for the extraction of emerging patterns in big data environments. E2PAMEA follows an adaptive schema to automatically employ different genetic operators according to the learning needs, which avoid the tuning of some parameters. It also em...

Real-time data analysis is becoming increasingly important in Big Data environments for addressing data stream issues. To this end, several technological frameworks have been developed, both open-source and proprietary, for the analysis of streaming data. This paper analyzes some open-source technological frameworks available for data streams, deta...

Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorised as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns are interesting in a data stream context as easy, fast, reliable decisions can be made. However, their extract...

Nowadays, the growth of available data, known as big data, and machine learning techniques are changing our lives. The extraction of insights related to the underlying phenomena in data is key in order to improve decision-making processes. These underlying phenomena are described in emerging pattern mining by means of the description of the discrim...

Background Emerging pattern mining is a data mining task that extracts rules describing discriminative relationships amongst variables. These rules should be understandable for the experts. Comprehensibility of a rule is traditionally determined by several objectives, which can be calculated by different measures. In this way, multi-objective evolu...

Emerging pattern mining is a data mining task that belongs to the supervised descriptive rule discovery framework. Its objective is to find rules that describe emerging behaviour or differentiating characteristics with respect to a property of interest. A Multi-Objective Evolutionary Algorithm for the Extraction of Fuzzy Emerging Patterns (MOEA-EFE...

The presence of noise in datasets to which data mining techniques are applied can greatly reduce the quality and interest of the knowledge extracted. Subgroup discovery is a supervised descriptive rule discovery technique which is not exempt from this problem. The aim of this paper is to improve the descriptions of subgroups previously obtained by...

Nowadays, there is an incredible increase of data volumes around the world, with the Internet as one of the main actors in this scenario and a growth rate above 30GB/s. The treatment of this huge amount of information cannot be carried out through traditional data mining algorithms in an efficient way and it is necessary to adapt and design new alg...

La minería de patrones emergentes es una tarea de minería de datos descriptiva bajo el paradigma del aprendizaje supervisado. A lo largo de la literatura se puede encontrar un amplio abanico de propuestas con buenos resultados, sin embargo no existe un amplio estudio enfocado mediante las metaheurísticas. En concreto, sólo encontramos unúnico model...

Subgroup discovery is a data mining task halfway between descriptive and predictive data mining. Nowadays it is very relevant for researchers due to the fact that the knowledge extracted is simple and interesting. For this task, evolutionary fuzzy systems are well suited algorithms because they can find a good trade-off between multiple objectives...

La búsqueda patrones emergentes persigue describir un problema mediante la obtención de tendencias emergentes en el tiempo, o la caracterización de diferencias entre clases o entre un grupo de variables. En este trabajo se presenta un nuevo modelo evolutivo para la obtención de patrones emergentes basado en un enfoque mono-objetivo, con la capacida...

El descubrimiento de subgrupos es una tarea de la minería de datos entre la clasificación y la descripciòn. Esta tarea es de gran interés para los investigadores debido a su éxito en campos como Medicina o Bioinformática. En este trabajo se describe un paquete de algoritmos para la extracción de reglas de descripción de subgrupos desarrollado ínteg...

This paper proposes a novel algorithm for subgroup discovery task based on genetic programming and fuzzy logic called Fuzzy Genetic Programming-based for Subgroup Discovery (FuGePSD). The genetic programming allows to learn compact expressions with the main objective to obtain rules for describing simple, interesting and interpretable subgroups. Th...

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 pr...

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 accurat...

Subgroup Discovery (SD) is a 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. General rules describing as many instances as possible are preferred in SD, but this can lead to less accurate descriptions that incorrectly describe some...

Subgroup discovery is a descriptive data mining technique which aims at obtaining interesting rules through supervised learning. In general, there are no works analysing the consequences of the presence of missing values in data in this task, although improper handling of this type of data in the analysis may introduce bias and can result in mislea...

In real-life data, a loss of information is frequent in data mining due to the presence of missing values in the attributes. Missing values can occur due to problems in the manual data entry procedures, equipment errors or incorrect measurements. The presence of missing values in attributes conditions the results obtained by any knowledge extractio...

The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm –one of the most representative evolutionary fuzzy systems for subgroup discovery–obtains preci...

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 t...

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 st...

This paper presents an experimental study with several subgroup discovery algorithms using data from a web-based education system. The main objective of this contribution is to extract unusual subgroups to describe possible relationships between the use of the e-learning platform and marks obtained by the students. The results obtained by the best...

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 ev...

A main purpose of a multi-objective evolutionary algorithm is to find a good relationship between convergence and diversity of the population. Convergence guides the algorithm to search the optimal solution and diversity tries to avoid a premature stagnation of the search. In multi-objective evolu-tionary algorithms, diversity has been promoted usi...

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 i...

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....

The interpretability of the results obtained and the quality measures used both to extract and evaluate the rules are two key aspects of subgroup discovery. In this study, we analyse the influence of the type of rule used to extract knowledge in subgroup discovery, and the quality measures more adapted to the evolutionary algorithms for subgroup di...

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 me...

This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student’s usage of the different activities and modules provided by this e-learn...

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial. Leída el 11 de enero de 2007

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 f...

The automatic classification of LOs into different categories enables us to search for, access, and reuse them in an effective and efficient way. Following this idea, in this paper, we focus specifically on how to automatically recommend the classification attribute of the IEEE LOM when a user adds a new LO to a repository. To do it, we propose the...

This paper presents a multiobjective genetic algorithm for obtaining fuzzy rules for subgroup discovery. 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 multiobjective algorithm proposed in this paper defines three objectives. One of them...

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...

Nowadays, face to face contact with the client continues to be fundamental to the development of marketing acts. Trade fairs are, in this sense, a basic instrument in company marketing policies, especially in Industrial Marketing. Due to the elevated investment in term of both time and money it is necessary the automatic extraction of relevant and...

This paper describes the use of data mining methods in e-learning system for providing feedback to courseware authors. The discovered information is presented in the form of prediction rules since these are highly comprehensible and they show important relationships among the presented data. The rules will be used to improve courseware, specially A...

Resumen En este trabajo se presenta una propuesta para la extracción de conocimiento en un problema del área de marketing, el estudio de la influencia que tienen las variables de planificación de un certamen ferial sobre el nivel de consecución de los objetivos planteados previamente para el mismo. En este problema el objetivo es extraer reglas que...

In this paper we present a proposal for knowledge extraction in a market problem, the study of the influence that trade fair planning variables have on the attainment level of objectives previously planned. In this problem the main objective is the extraction of rules which describe subgroups contributing relevant information on them. The evolution...

Resumen En este trabajo presentamos un estudio prelimi-nar sobre la influencia de distintos aspectos en el comportamiento del modelo evolutivo SDIGA de extracción de reglas difusas de des-cripción de subgrupos. El estudio se centra en la influencia de las medidas de calidad utilizadas por el algoritmo, el tipo de regla con el que se representa el c...