Association Rule Filter for Data Mining in Call Tracking Data

Kazunori MATSUMOTO  Kazuo HASHIMOTO  

Publication
IEICE TRANSACTIONS on Communications   Vol.E81-B   No.12   pp.2481-2486
Publication Date: 1998/12/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Issue on the Latest Development of Telecommunication Research)
Category: Network Design, Operation, and Management
Keyword: 
knowledge discovery and datamining,  association rule,  filtering,  Akaike's information theory,  

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Summary: 
Call tracking data contains a calling address, called address, service type, and other useful attributes to predict a customer's calling activity. Call tracking data is becoming a target of data mining for telecommunication carriers. Conventional data-mining programs control the number of association rules found with two types of thresholds (minimum confidence and minimum support), however, often they generate too many association rules because of the wide variety of patterns found in call tracking data. This paper proposes a new method to reduce the number of generated rules. The method proposed tests each generated rule based on Akaike Information Criteria (AIC) without using conventional thresholds. Experiments with artificial call tracking data show the high performance of the proposed method.