Telecom customer clustering via glowworm swarm optimisation algorithm. (19th September 2019)

• Record Type: Journal Article
• Authors: Liu, Yanli; Zhu, Mengyu
• Is Part Of: International journal of high performance computing and networking. Volume 14:Number 4(2019)
• Page Start: 397
• Page End: 404
• Publication Date: 2019-09-19
• Subjects: glowworm swarm optimisation -- customer's subdivision -- data clustering -- high performance computing; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

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.