Title: Clustering algorithm for mixed attributes data based on glowworm swarm optimisation algorithm and K-prototypes algorithm

Authors: Yaping Li; Zhiwei Ni; Weiliang Zhou

Addresses: School of Management, Hefei University of Technology, Anhui, Hefei, 230009, China; Postdoctoral Workstation, Party School of Anhui Provincial Committee of the Communist Party of China (Anhui Academy of Governance), Anhui, Hefei, 230022, China ' School of Management, Hefei University of Technology, Anhui, Hefei, 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Anhui, Hefei, 230009, China ' Postdoctoral Workstation, Party School of Anhui Provincial Committee of the Communist Party of China (Anhui Academy of Governance), Anhui, Hefei, 230022, China

Abstract: The main purpose of this research is to improve the clustering accuracy of mixed attributes data. Therefore, glowworm swarm optimisation (GSO) algorithm is introduced into K-prototypes algorithm to form a new clustering algorithm. First, GSO algorithm is improved by using the good point set. Then, the improved GSO algorithm is employed to search extreme points of density in the space of data objects. The initial clustering centre of K-prototypes algorithm is chosen from the extreme points of density. Meanwhile, a unified method is designed for the distance of numeric data and categorical data. On this basis, a new clustering algorithm flow (GSOKP) is designed. Finally, the UCI datasets of numeric data, categorical data and mixed data are selected to test GSOKP algorithm. And the effectiveness of GSOKP algorithm is analysed in terms of clustering accuracy through experimental comparison.

Keywords: GSO algorithm; K-prototypes algorithm; good point set; clustering.

DOI: 10.1504/IJBIC.2021.118095

International Journal of Bio-Inspired Computation, 2021 Vol.18 No.2, pp.105 - 113

Received: 20 Aug 2020
Accepted: 28 Dec 2020
Published online: 12 Oct 2021 *

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