Journal of Information Processing Systems

  • 제6권2호
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  • Pages.163-176
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  • 2010
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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IMTAR: Incremental Mining of General Temporal Association Rules

  • Dafa-Alla, Anour F.A. (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Shon, Ho-Sun (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Saeed, Khalid E.K. (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Piao, Minghao (Database/Bioinformatics Lab., Chungbuk National University) ;
  • Yun, Un-Il (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Cheoi, Kyung-Joo (School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Keun-Ho (Database/Bioinformatics Lab., Chungbuk National University)
  • 투고 : 2010.02.02
  • 심사 : 2010.04.06
  • 발행 : 2010.06.30
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초록

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

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참고문헌

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피인용 문헌

  1. Effect of Temporal Relationships in Associative Rule Mining for Web Log Data vol.2014, 2014, https://doi.org/10.1155/2014/813983
  2. Incremental mining of weighted maximal frequent itemsets from dynamic databases vol.54, 2016, https://doi.org/10.1016/j.eswa.2016.01.049