Open AccessAutomatic clustering method based on evolutionary optimisation
Author(s) -
Liu Cong,
Zhou Aimin,
Zhang Guixu
Publication year - 2013
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0187
Subject(s) - cluster analysis , computer science , coding (social sciences) , data mining , consistency (knowledge bases) , determining the number of clusters in a data set , artificial intelligence , fuzzy clustering , rand index , evolutionary algorithm , mathematics , cure data clustering algorithm , statistics
How to set the cluster number plays a key role in many clustering applications. To address this issue, this study introduces an automatic clustering method based on evolutionary algorithms (EAs). The basic idea is to convert a clustering problem into a global optimisation problem and tackle it by an EA. A new validity index, which balances the inter‐cluster consistency and the intra‐cluster consistency, is proposed to be the objective function. Three adaptive coding schemes, which can deal with variable‐length optimisation problems by using a fixed‐length chromosome, are designed to detect the cluster number automatically. The validity index and adaptive coding schemes are incorporated in an EA for automatic clustering. The authors approach is compared with some widely used validity indices and an adaptive coding scheme on some artificial data sets and two real‐world problems. The experimental results suggest that their method not only successfully detects the correct cluster numbers but also achieve stable results for most of test problems.