Cristóbal J. Carmona

Universidad de Jaén | UJAEN · Department of Computer Sciences

Nowadays the amount of networks of devices and sensors, such as smart homes or smart cities, is rapidly increasing. Each of these devices generates massive amounts of data on a continuous basis where an interpretable description of its state is interesting for the experts. This knowledge can be extracted by means of emerging pattern mining techniqu...

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...

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...

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...

To date, the subgroup discovery task has been considered in problems where a target variable is unequivocally described by a set of features, also known as instance. Nowadays, however, with the increasing interest in data storage, new data structures are being provided such as the multiple-instance data in which a target variable value is ambiguous...

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...

Emerging pattern mining is a data mining task that aims to discover discriminative patterns, which can describe emerging behavior with respect to a property of interest. In recent years, the description of datasets has become an interesting field due to the easy acquisition of knowledge by the experts. In this review, we will focus on the descripti...

The search of emerging patterns pursues the description of a problem through the obtaining of trends in the time, or characterisation of differences between classes or group of variables. This contribution presents an application to a real-world problem related to the photovoltaic technology through the algorithm EvAEP. Specifically, the algorithm...

Supervised descriptive rule discovery represents a set of data mining techniques whose objective is to describe data with respect to a property of interest. This concept encompasses different techniques such as subgroup discovery, emerging patterns and contrast sets. Supervised learning is used to obtain rules for descriptive purposes but with diff...

Imbalanced classification is related to those problems that have an uneven distribution among classes. In addition to the former, when instances are located into the overlapped areas, the correct modeling of the problem becomes harder. Current solutions for both issues are often focused on the binary case study, as multi-class datasets require an a...

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...

External factors such as the presence of noise in data can affect the data mining process. This is a common problem that produces several negative consequences which involves errors in the data collection, preparation and, above all, in the results obtained by the data mining techniques employed. The capabilities of the models built under such circ...

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...

Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that...

The Concentrating Photovoltaic technology is focused on the generation of electricity reducing the associated costs. The main characteristics is to concentrate the sunlight in solar cells by means of optical device such as plastic or glass material. This technology could contribute with several benefits to our environmental. This paper presents a n...

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...

Concentrating photovoltaics is an innovative alternative to flat-plate module to produce cost-competitiveness electricity. It is based on the use of optical system of reduced cost which is able to concentrate the solar light on a very small surface (high efficiency solar cell). At present, this technology has a marginal position in photovoltaic mar...

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...

The presence of noise in data is a common problem that produces several negative consequences, and is an unavoidable problem, which affects the data collection and data preparation processes in Data Mining applications, where errors commonly occur. The performance of the models built under such circumstances will heavily depend on the quality of th...

In data mining, the process of data obtained from users history databases is called Web usage mining. The main benefits lie in the improvement of the design of Web applications for the final user. This paper presents the application of subgroup discovery (SD) algorithms based on evolutionary fuzzy systems (EFSs) to the data obtained in an e-commerc...

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...

Extraction of biologically-meaningful knowledge is one of the important and challenging tasks in bioinformatics, in particular computational analysis of DNA and protein sequences, in order to identify biological function(s) and behaviour(s) of newly-extracted sequences. Computational intelligence techniques in corporation with sequence-driven featu...

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...

Web usage mining is the process of extracting useful information from users history databases associated to an e-commerce website. The extraction is usually performed by data mining techniques applied on server log data or data obtained from specific tools such as Google Analytics. This paper presents the methodology used in an e-commerce website o...

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...

The Discretization, as a data preprocessing technique, has played an important role in many areas such as artificial intelligence, data mining and machine learning. In this paper, we propose the use of evolutionary algorithms to select a subset of cut points that defines the best possible discretization scheme of a data set. First, we identify the...

Subgroup discovery is a descriptive data mining technique whose main objective is the search for partial relations with unusual statistical characteristics with respect to a property of interest. In this paper, we present the application of a subgroup discovery technique in a users history data set associated to an e-commerce website called www.OrO...

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...

The nested generalized exemplar theory accomplishes learning by storing objects in Euclidean n-space, as hyperrectangles. Classification of new data is performed by computing their distance to the nearest “generalized exemplar” or hyperrectangle. This learning method allows the combination of the distance-based classification with the axis-parallel...

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...

Evolutionary Computation is a typical paradigm for the Radial Basis Function Network design. In this environment an individual represents a whole network. An alternative is to use cooperative-competitive methods where an individual is a part of the solution. CO²RBFN is an evolutionary cooperative-competitive hybrid methodology for the de...

Learning in imbalanced domains is one of the recent challenges in machine learning and data mining. In imbalanced classification, data sets present many examples from one class and few from the other class, and the latter class is the one which receives more interest from the point of view of learning. One of the most used techniques to deal with t...

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....

In this paper an adaptation of CO2RBFN, evolutionary COoperative- COmpetitive algorithm for Radial Basis Function Networks design, applied to the prediction of the extra-virgin olive oil price is presented. In this algorithm each individual represents a neuron or Radial Basis Function and the population, the whole network. Individuals compite for s...

This paper presents the adaptation of CO 2 RBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra vir- gin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire popul...

Among the variety of approaches for developing therapies for the blind, electrical neurostimulation of the visual pathways seems to be a promising choice. Delivering bi-phasic bioelectric pulses to the nerves implies the selection of values for a number of parameters within a wide range. This needs to be done for every implanted electrode, and for...

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...

In this paper a multiobjective optimization algorithm for the design of Radial Basis Function Networks is proposed. The goal of the design algorithm is to obtain networks with a high tradeoff between accuracy and complexity, overcoming the drawbacks of the traditional single objective evolutionary algorithms. The main features of EMORBFN are a sele...

The information developed pertaining to biodiversity studies tends to be scattered around many bibliographic references. A review of the Nordiidae family is being carried out, but the very data to be collected does not allow systematic access. The Nordiidae family, belonging to the animal taxon Nematodes, shows high diversity and an extraordinary u...

Resumen En este artículo se presenta un nuevo elemento poblacional a considerar en el diseño de algoritmos evolutivos multiobjetivo para la optimización de Redes de Funciones de Base Radial. Concretamente, se divide la población en subpoblaciones virtuales, donde cada subpoblación está compuesta por individuos o redes con el mismo número de neurona...

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...