EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework

被引:0
作者
Qaddoura R. [1 ]
Faris H. [2 ]
Aljarah I. [2 ]
Castillo P.A. [3 ]
机构
[1] Information Technology, Philadelphia University, Amman
[2] King Abdullah II School for Information Technology, The University of Jordan, Amman
[3] ETSIIT-CITIC, University of Granada, Granada
关键词
Cluster analysis; Clustering; Evolutionary computing; Framework; !text type='Python']Python[!/text;
D O I
10.1007/s42979-021-00511-0
中图分类号
学科分类号
摘要
EvoCluster is an open source and cross-platform framework implemented in Python language, which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. This paper is an extension to the existing EvoCluster framework in which it includes different distance measures for the objective function, different techniques of detecting the k value, and a user option to consider either supervised or unsupervised datasets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, twelve evaluation measures, more than twenty distance measures, and ten different ways for detecting the k value. The source code of EvoCluster is publicly available at http://evo-ml.com/evocluster/. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.
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