A Short Survey on Data Clustering Algorithms

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis. Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand. In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed. Advanced clustering algorithms are also discussed. After that, the existing clustering evaluation metrics are reviewed. A summary with future insights is provided at the end

Estudio comparativo de algoritmos de agrupación basados en la ley de gravitación universal aplicados a la segmentación de imágenes

Esta investigación tuvo como objetivo principal resolver el problema de la segmentación de imágenes a través del uso del algoritmo de agrupación basados en la ley de gravitación universal llamado “Algoritmo de búsqueda gravitacional”, se realizó un análisis comparativo entre los resultados obtenidos por este y los obtenidos por los algoritmos de agrupación “convencionales”, cuyo propósito fue evaluar si este resuelve las debilidades identificadas en los algoritmos convencionales para este campo de aplicación.This study's main objective was to solve the problem of image segmentation through the use of clustering algorithms based on the law of universal gravitation called "gravitational search algorithm with heuristics" and a comparative analysis of the results was carried out by this and those obtained by conventional algorithms, whose purpose was to assess whether this resolves the weaknesses identified in conventional algorithms for this field of application