A multi-objective genetic graph-based clustering algorithm with memory optimization

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. H. D. Menéndez, D. F. Barrero, and D. Camacho, "A multi-objective genetic graph-based clustering algorithm with memory optimization", in 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3174 - 3181Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.This work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872

Clustering Algorithms: An Exploratory Review

A process of similar data items into groups is called data clustering. Partitioning a Data Set into some groups based on the resemblance within a group by using various algorithms. Partition Based algorithms key idea is to split the data points into partitions and each one replicates one cluster. The performance of partition depends on certain objective functions. Evolutionary algorithms are used for the evolution of social aspects and to provide optimum solutions for huge optimization problems. In this paper, a survey of various partitioning and evolutionary algorithms can be implemented on a benchmark dataset and proposed to apply some validation criteria methods such as Root-Mean-Square Standard Deviation, R-square and SSD, etc., on some algorithms like Leader, ISODATA, SGO and PSO, and so on

Genetic graph-based in clustering applied to static and streaming data analysis

Tesis inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: diciembre de 2014Unsupervised Learning Techniques have been widely used in Data Mining over the last few years. These techniques try to identify patterns in a dataset blindly. Clustering is one of the most promising elds in Unsupervised Learning. It consists on grouping the data by similarity. This eld has generated several research works which have tried to deal with di erent problems related to the pattern extraction and data grouping processes. One of the most innovative clustering methodologies is shape-based or continuity-based clustering which tries to group data according to the form they de ne in the space. This dissertation is focused on how to apply Genetic Algorithms to the continuitybased clustering problems. Genetic Algorithms have been traditionally used in optimization problems. They are featured by an encoding -which represents the solution space; a population set of chromosomes -which are the potential solutions; and some genetic operations -which are used to evolve the solutions in order to nd the best chromosome or solution. The main idea is to take advantage of their potential, generating new algorithms which can improve the performance of classical clustering algorithms, and apply them to static and streaming data. In order to design these algorithms, this dissertation has been based on the Spectral Clustering algorithm. This algorithm studies the spectrum of a Similarity Graph in order to de ne the clusters. The clusters de ned by Spectral Clustering usually respect the data continuity. Using this idea as a starting point, di erent graph-based genetic algorithms have been designed to deal with the continuity-based clustering problem. The di erent algorithms developed have been divided in three generations: The rst generation is based on genetic graph-based clustering algorithms. In this generation we combined graph-based clustering and genetic algorithms to generate a graph topology among the data, in order to nd the best way to cut the graph. This cutting process is used to discriminate the nal clusters. The main idea is to use hybrid algorithms which combine di erent metrics extracted from graph theory. In order to evaluate the performance on real-world problems, these algorithms have been also applied to text summarization. The second generation is based on multi-objective genetic graph-based clustering algorithms. This generation introduces the Pareto Front generated by the di erent tness functions used in the genetic search. The Pareto Front is used to study the solution space and provides more robust and accurate solutions. During this generation we also used co-evolutionary algorithms to include the number of clusters in the search space. Finally, the last generation is focused on large and streaming data analysis. During this generation the previous algorithms have been adapted to deal with large data, combining di erent methodologies such as online clustering and MapReduce. The main idea is to study their performance compared with other algorithms. The dissertation also includes a description of other graph-based bio-inspired algorithms, in this case Ant Colony Optimization Clustering algorithms, which have been designed during the dissertation, in order to extend the range of study to other bio-inspired areas. Finally, with the purpose of evaluating the algorithms of the di erent generations, we have compared them with relevant and well-known clustering algorithms using synthetic and real-world datasets extracted from the literature and the UCI Machine Learning RepositoryLas técnicas de aprendizaje no supervisado han sido ampliamente utilizadas en minería de datos en los últimos años. Estas técnicas tratan de extraer patrones de un conjunto de datos de forma ciega. Dentro de las mismas, el Clustering es uno de los campos más prometedores. Este consiste en la agrupación de los datos por similitud. Este campo ha generado varios trabajos de investigación que han tratado de hacer frente a diferentes problemas relacionados con la extracción de patrones y los procesos de agrupación de datos. Una de las metodologías de clustering más innovadoras se basa en agrupar los datos por continuidad, respetando la forma que estos definen en espacio en el que se encuentran. Esta tesis se centra en la manera de aplicar algoritmos genéticos a los problemas de clustering basado en continuidad. Los algoritmos genéticos han sido utilizados tradicionalmente en problemas de optimización. Se caracterizan por una codificación -que representa el espacio de soluciones-, una población o conjunto de cromosomas -que son las soluciones potenciales dentro de este espacio-, y algunas operaciones genéticas -que se utilizan para evolucionar las soluciones con el fin de encontrar el mejor cromosoma o solución-. La idea principal es aprovechar el pontencial de los algoritmos genéticos generando nuevos algoritmos que pueden mejorar el rendimiento de los algoritmos clásicos aplicados tanto a datos estáticos como a flujos continuos de datos. De cara a diseñaar estos algoritmos, esta tesis doctoral utiliza el algoritmo de Spectral Clustering como punto de partida. Este algoritmo estudia el espectro de un grafo de similitud con el fin de dfinir las agrupaciones o clusters. Los grupos de nidos por Spectral Clustering suelen respetar la continuidad de los datos. Utilizando esta idea, se han diseñado diferentes algoritmos genéticos basados en grafos para hacer frente al problema de agrupación basada en continuidad. Los diferentes algoritmos desarrollados se han dividido en tres generaciones: La primera generación se basa en algoritmos de clustering genéticos basados en grafos. En esta generación se han combinado técnicas de Graph Clustering y algoritmos genéticos para generar una topología de grafo entre los datos, con el fin de encontrar la mejor manera de cortar el grafo. Este proceso de corte se utiliza para discriminar los grupos finales. La idea principal es utilizar algoritmos híbridos que combinan diferentes métricas extraídas de teoría de grafos. Con el fin de evaluar el comportamiento de los algoritmos en problemas del mundo real, estos algoritmos se han aplicado al problema de cómo generar resúmenes automáticos. La segunda generación se basa en algoritmos multi-objetivo de clustering genético basado en grafos. Esta generación introduce el Frente de Pareto, generado por las diferentes funciones de fitness utilizadas en la búsqueda genética. El frente de Pareto se utiliza para estudiar el espacio de soluciones y proporcionar soluciones más robustas y precisas. Durante esta generación también utilizamos algoritmos co-evolutivos de cara a incluir el número de clusters en el espacio de búsqueda Finalmente, la ultima generación se centra en el análisis de grandes cantidades y flujos de datos. Durante esta generación los algoritmos anteriormente mencionados se han adaptado para hacer frente a grandes volúmenes de datos, combinando diferentes metodologí as como el clustering online y MapReduce. La idea principal es estudiar su rendimiento en comparación con otros algoritmos. La tesis también incluye aportaciones de otros algoritmos bio-inspirados basados en grafos, en este caso, algoritmos de clustering usando optimización por colonias de hormigas. Estos algoritmos han sido diseñados durante el desarrollo de la tesis para ampliar el rango de estudio a otros entornos bio-inspirados. Por último, con el fin de evaluar los algoritmos de las diferentes generaciones, se han comparado con algoritmos de clustering conocidos. El rendimiento de estos algoritmos se ha medido utilizando conjuntos de datos sintéticos y reales extraídos de la literatura y del repositorio UCI de Machine Learning