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Modelling Behavior Patterns in Cellular Networks

Authors:
Elena Ravve
Zeev Volkovich
Katerina Korenblat
Valery Kirzner
Thomas Couronne

Keywords: Consumer behavior pattern; Market segmentation; Probability distribution; Mixture distribution model; Machine learning; Unsupervised classification; Clustering.

Abstract:
In this paper, we explore customer behavior in cellular networks. We develop a novel model of the fundamental user profiles. The study is based on investigation of activities of millions of customers of Orange, France. We propose a way of decomposition of the observed distributions according to certain external criteria. We analyze distribution of customers having the same number of calls during a fixed period. A segmentation of the population is provided by an approximation of the considered distribution by means of a mixture of several more "basic" distributions presenting the "granularity" of the user's activity. In order to examine the meaning of the found approximation, a clustering of the customers is provided using their daily activity, and a new clustering procedure is constructed. The optimal number of clusters turned out to be three. The approximation is the reduced in the optimal partition to a single-exponential one in one of the clusters and to two double-exponential in others. This fact confirms that the proposed partition corresponds to reliable consequential social groups.

Pages: 67 to 72

Copyright: Copyright (c) IARIA, 2016

Publication date: November 13, 2016

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-513-5

Location: Barcelona, Spain

Dates: from November 13, 2016 to November 17, 2016