Association Rule Filter for Data Mining in Call Tracking Data

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概要

• 論文の詳細を見る

• Call tracking data contains calling address, called address, service type, and other useful attributes to predict 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 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.

• 一般社団法人電子情報通信学会の論文
• 1998-01-20

著者

• Hashimoto Kazuo

  Kdd R&d Laboratories

• Matsumoto Kazunori

  Kdd R&d Laboratories

関連論文

• Association Rule Filter for Data Mining in Call Tracking Data (Special Issue on the Latest Development of Telecommunication Research)
• A Prototype of an Automatic Japanese-Korean Interpretation System (特集:音声言語情報処理)
• Formulating International Telephone Network Management as Distributed Problem Solving
• Association Rule Filter for Data Mining in Call Tracking Data

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