LEAC: An efficient library for clustering with evolutionary algorithms

被引：7

作者：

Robles-Berumen, Hermes ^{[1
]}

Zafra, Amelia ^{[2
]}

Fardoun, Habib M. ^{[3
]}

Ventura, Sebastian ^{[2
,3
,4
]}

机构：

[1] Autonomous Univ Zacatecas, Elect Engn & Earth Sci, Zacatecas, Mexico

[2] Univ Cordoba, Dept Comp Sci & Numer Anal, Cordoba, Spain

[3] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Syst, Jeddah, Saudi Arabia

[4] Maimonides Inst Biomed, Knowledge Discovery & Intelligent Syst Biomed Lab, Cordoba, Spain

来源：

KNOWLEDGE-BASED SYSTEMS | 2019年 / 179卷

关键词：

Clustering; C plus plus library; Evolutionary algorithms; Genetic algorithms; Software;

D O I：

10.1016/j.knosys.2019.05.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces LEAC, a new C++ partitioning clustering library based on evolutionary computation. LEAC provides plenty of elements (individual encoding schemes, genetic operators, evaluation metrics, among others) which allow an easy and fast development of new clustering algorithms. Furthermore, it includes 23 algorithms which represent the state-of-the-art in Evolutionary Algorithms for partial clustering. The paper describes through examples the main features and the design principles of the software, as well as how to use LEAC to carry out a comparison between different proposals and how to extend it by including new algorithms. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：117 / 119

页数：3

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