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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15788
Title: Essence-Based Clustering: A multi-strategic and highly-customizable clustering approach
Authors: Rodríguez Siu, Kevin Christian
Barrios Aranibar, Dennis
Patiño Escarcina, Raquel Esperanza
Keywords: Approximation algorithms;Artificial intelligence;Based clustering;Clustering approach;Clustering problems;Customizable;Data repositories;Multi-strategic;Multiple strategy;Clustering algorithms
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018172586&doi=10.1109%2fLA-CCI.2016.7885724&partnerID=40&md5=d0dc545ae3c2b969b5c229f55d792cab
Abstract: The choice of a good clustering algorithm is vital in many tasks to optimize results. Nowadays, the most used algorithms use only one strategy to find and form the clusters of data, which can limit the effectiveness of the process. This paper presents a new approximation to clustering, called Essence-Based Clustering, that combines multiple strategies in a series of steps, allowing two levels of configuration of parameters, both for the whole algorithm and for each strategy used on its own. Experimental results in known data repositories show that this approach is well suited for solving clustering problems and it can do it with equivalent or better results than the current approaches. © 2016 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15788
ISBN: urn:isbn:9781509051052
Appears in Collections:Artículos - Ciencia de la computación

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