Efficient algorithms based on the k-means and Chaotic League Championship Algorithm for numeric, categorical, and mixed-type data clustering
作者：
Highlights：
• The search algorithm C-LCA is done by adding two chaotic parameters into the LCA.
• The search clustering using CS-LCA and the KSC-LCA algorithms are proposed.
• The Gower distance and a mechanism are adopted for handling the mixed-type data.
• The search clustering CS-LCA ranks first for the pure categorical data.
• The KSC-LCA ranks first for the pure numeric and mixed-type data.
摘要
•The search algorithm C-LCA is done by adding two chaotic parameters into the LCA.•The search clustering using CS-LCA and the KSC-LCA algorithms are proposed.•The Gower distance and a mechanism are adopted for handling the mixed-type data.•The search clustering CS-LCA ranks first for the pure categorical data.•The KSC-LCA ranks first for the pure numeric and mixed-type data.
论文关键词：Data clustering,Search clustering algorithm,Hybrid clustering algorithm,League Championship Algorithm (LCA),Chaos optimization algorithms (COA),Mixed-type data
论文评审过程：Received 13 February 2017, Revised 31 July 2017, Accepted 1 August 2017, Available online 2 August 2017, Version of Record 17 August 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.08.004
