Title: Telecom customer clustering via glowworm swarm optimisation algorithm

Authors: Yanli Liu; Mengyu Zhu

Addresses: School of Information Engineering, East China Jiaotong University, Nanchang 330013, China ' School of Information Engineering, East China Jiaotong University, Nanchang 330013, China

Abstract: The glowworm swarm optimisation (GSO) algorithm is a novel algorithm with the simultaneous computation of multiple optima of multimodal functions. Data-clustering techniques are classification algorithms that have a wide range of applications. Since K-means algorithm is easy to fall into the local optimum by the selection of initial clustering centre, GSO algorithm is used for the telecom customers clustering. The customer consumption data is extracted by means of the recency frequency monetary (RFM) model and the standardised data is clustered automatically using the GSO algorithm's synchronous optimisation ability. In the clustering optimisation algorithm, adaptive step size is used instead of the original fixed step size to avoid local optimisation of the algorithm and obtain higher accuracy. Compared with K-means clustering algorithm, GSO approach can automatically generate the number of clusters and use RFM model to reduce effectively the size of the data processing. The results of the experiments demonstrate that the GSO-based clustering technique is a promising technique for the data clustering problems.

Keywords: glowworm swarm optimisation; customer's subdivision; data clustering; high performance computing.

DOI: 10.1504/IJHPCN.2019.102345

International Journal of High Performance Computing and Networking, 2019 Vol.14 No.4, pp.397 - 404

Received: 31 Jul 2017
Accepted: 21 Dec 2017

Published online: 19 Sep 2019 *

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