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Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining

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Advances in Knowledge Discovery and Data Mining (PAKDD 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2637))

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Abstract

The discovery of association relationship among the data in a huge database has been known to be useful in selective marketing, decision analysis, and business management. A significant amount of research effort has been elaborated upon the development of efficient algorithms for data mining. However, without fully considering the time-variant characteristics of items and transactions, it is noted that some discovered rules may be expired from users’ interest. In other words, some discovered knowledge may be obsolete and of little use, especially when we perform the mining schemes on a transaction database of short life cycle products. This aspect is, however, rarely addressed in prior studies. To remedy this, we broaden in this paper the horizon of frequent pattern mining by introducing a weighted model of transaction-weighted association rules in a time-variant database. Specifically, we propose an efficient Progressive Weighted Miner (abbreviatedly as PWM) algorithm to perform the mining for this problem as well as conduct the corresponding performance studies. In algorithm PWM, the importance of each transaction period is first reflected by a proper weight assigned by the user. Then, PWM partitions the time-variant database in light of weighted periods of transactions and performs weighted mining. Algorithm PWM is designed to progressively accumulate the itemset counts based on the intrinsic partitioning characteristics and employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. With this design, algorithm PWM is able to efficiently produce weighted association rules for applications where different time periods are assigned with different weights and lead to results of more interest.

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Author information

Authors and Affiliations

  1. BenQ Corporation 18, Jihu Road, Neihu, Taipei, 114, Taiwan, R.O.C.

    Chang-Hung Lee

  2. Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, ROC

    Jian Chih Ou & Ming-Syan Chen

Authors
  1. Chang-Hung Lee
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  2. Jian Chih Ou
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  3. Ming-Syan Chen
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Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea

    Kyu-Young Whang

  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea

    Jongwoo Jeon

  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea

    Kyuseok Shim

  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA

    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Lee, CH., Ou, J.C., Chen, MS. (2003). Progressive Weighted Miner: An Efficient Method for Time-Constraint Mining. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_45

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  • DOI: https://doi.org/10.1007/3-540-36175-8_45

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04760-5

  • Online ISBN: 978-3-540-36175-6

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