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Updating generalized association rules with evolving taxonomies

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Abstract

Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model for data mining. Earlier work on mining generalized association rules, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the analysts might have to dynamically adjust the taxonomies from different viewpoints so as to discover more informative rules. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called Diff_ET and Diff_ET2, to update the discovered frequent itemsets. Empirical evaluation shows that the proposed algorithms are very effective and have good linear scale-up characteristics.

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References

  1. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings 1993 ACM-SIGMOD international conference management of data, pp 207–216

  2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings 20th international conference very large data bases, pp 487–499

  3. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. SIGMOD Rec 26:255–264

    Article  Google Scholar 

  4. Cheung DW, Han J, Ng VT, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental update technique. In: Proceedings 1996 international conference data engineering, pp 106–114

  5. Cheung DW, Ng VT, Tam BW (1996) Maintenance of discovered knowledge: a case in multi-level association rules. In: Proceedings 1996 international conference knowledge discovery and data mining, pp 307–310

  6. Cheung DW, Lee SD, Kao B (1997) A general incremental technique for maintaining discovered association rules. In: Proceedings DASFAA’97, pp 185–194

  7. Czejdo B, Morzy M, Wojciechowski M, Zakrzewicz M (2002) Materialized views in data mining. In: Proceedings 13th international workshop on database and expert systems applications, pp 827–831

  8. Fang M, Shivakumar N, Garcia-Molina H, Motwani R, Ullman JD (1998) Computing iceberg queries efficiently. In: Proceedings 24th international conference very large data bases, pp 299–310

  9. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39(11):27–34

    Article  Google Scholar 

  10. Han J (1997) OLAP mining: an integration of OLAP with data mining. In: Proceedings IFIP conference data semantics, pp 1–11

  11. Han J, Cai Y, Cercone N (1992) Knowledge discovery in databases: an attribute-oriented approach. In: Proceedings 18th international conference very large data bases, pp 547–559

  12. Han J, Fu Y (1994) Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. In: Proceedings AAAI’94 workshop on knowledge discovery in databases (KDD’94), pp 157–168

  13. Han J, Fu Y (1995) Discovery of multiple-level association rules from large databases. In: Proceedings 21st international conference very large data bases, pp 420–431

  14. Hong TP, Wang CY, Tao YH (2000) Incremental data mining based on two support thresholds. In: Proceedings 4th international conference knowledge-based intelligent engineering systems and allied technologies, pp 436–439

  15. Kamber M, Han J, Chiang JY (1997) Metarule-guided mining of multidimensional association rules using data cubes. In: Proceedings 3rd international conference knowledge discovery and data mining (KDD’97), pp 207–210

  16. Lin WY, Su JH, Tseng MC (2002) OMARS: The framework of an online multi-dimensional association rules mining system. In: ICEB 2nd international conference electronic business, Taipei, Taiwan

  17. Ng KK, Lam W (2000) Updating of association rules dynamically. In: Proceedings 1999 international symposium on database applications in non-traditional environments, pp 84–91

  18. Park JS, Chen MS, Yu PS (1995) An effective hash-based algorithm for mining association rules. In: Proceedings 1995 ACM SIGMOD international conference management of data, San Jose, CA, USA, pp 175–186

  19. Savasere A, Omiecinski E, Navathe S (1995) An efficient algorithm for mining association rules in large databases. In: Proceedings 21st international conference very large data bases, pp 432–444

  20. Sarda NL, Srinivas NV (1998) An adaptive algorithm for incremental mining of association rules. In: Proceedings 9th international workshop on database and expert systems applications, pp 240–245

  21. Srikant R, Agrawal R (1995) Mining generalized association rules. In: Proceedings 21st international conference very large data bases, pp 407–419

  22. Thomas S, Bodagala S, Alsabti K, Ranka S (1997) An efficient algorithm for the incremental updation of association rules in large databases. In: Proceedings 3rd international conference knowledge discovery and data mining

  23. Tseng MC, Lin WY (2004) Maintenance of generalized association rules with multiple minimum supports. Intell Data Anal 8:417–436

    Google Scholar 

  24. Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(2):372–390

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Institute of Information Engineering, I-Shou University, Kaohsiung, 840, Taiwan

    Ming-Cheng Tseng

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan

    Wen-Yang Lin

  3. Department of Information Management, I-Shou University, Kaohsiung, 840, Taiwan

    Rong Jeng

Authors
  1. Ming-Cheng Tseng
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  2. Wen-Yang Lin
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  3. Rong Jeng
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Correspondence to Wen-Yang Lin.

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Tseng, MC., Lin, WY. & Jeng, R. Updating generalized association rules with evolving taxonomies. Appl Intell 29, 306–320 (2008). https://doi.org/10.1007/s10489-007-0096-5

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  • DOI: https://doi.org/10.1007/s10489-007-0096-5

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