An Immune Systems Approach for Classifying Mobile Phone Usage

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Author(s): Hanny Yulius Limanto (Nanyang Technological University, Singapore), Tay Joc Cing (Nanyang Technological University, Singapore)and Andrew Watkins (Mississippi State University, USA)
Copyright: 2008
Pages: 11
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch216

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

With the recent introduction of third generation (3G) technology in the field of mobile commu-nications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With these usage patterns, the service provider can effectively target certain classes of customers who are more likely to purchase their subscription plans. To discover these patterns, we use a novel algorithm called Artificial Immune Recognition System (AIRS) that is based on the specificity of the human immune system. In our experiment, the algorithm performs well, achieving an accuracy rate in the range of 80% to 90%, depending on the set of parameter values used.

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An Immune Systems Approach for Classifying Mobile Phone Usage

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