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Association Rule Filter for Data Mining in Call Tracking Data
Kazunori MATSUMOTO Kazuo HASHIMOTO
IEICE TRANSACTIONS on Communications
Publication Date: 1998/12/25
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
knowledge discovery and datamining, association rule, filtering, Akaike's information theory,
Full Text: PDF(472.5KB)>>
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.