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An Immune Systems Approach for Classifying Mobile Phone Usage

An Immune Systems Approach for Classifying Mobile Phone Usage

Hanny Yulius Limanto, Tay Joc Cing, Andrew Watkins
Copyright: © 2007 |Volume: 3 |Issue: 2 |Pages: 13
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202089|EISSN: 1548-3924|DOI: 10.4018/jdwm.2007040105
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MLA

Limanto, Hanny Yulius, et al. "An Immune Systems Approach for Classifying Mobile Phone Usage." IJDWM vol.3, no.2 2007: pp.54-66. http://doi.org/10.4018/jdwm.2007040105

APA

Limanto, H. Y., Cing, T. J., & Watkins, A. (2007). An Immune Systems Approach for Classifying Mobile Phone Usage. International Journal of Data Warehousing and Mining (IJDWM), 3(2), 54-66. http://doi.org/10.4018/jdwm.2007040105

Chicago

Limanto, Hanny Yulius, Tay Joc Cing, and Andrew Watkins. "An Immune Systems Approach for Classifying Mobile Phone Usage," International Journal of Data Warehousing and Mining (IJDWM) 3, no.2: 54-66. http://doi.org/10.4018/jdwm.2007040105

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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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