Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)

The Korean Operations Research and Management Science Society (한국경영과학회)
권호 표지

A Study on the Advanced Association Rules Algorithm of n-Items

개선된 n-항목 연관 규칙 알고리즘 연구

  • 황현숙 (부경대학교 경영정보학과) ;
  • 어윤양 (부경대학교 경영학부)
  • Published : 2002.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

The transaction tables of the existing association algorithms have two column attributes : It is composed of transaction identifier (Transaction_id) and an item identifier (item). In this kind of structure, as the volume of data becomes larger, the performance for the SQL query statements came applicable decreases. Therefore, we propose advanced association rules algorithm of n-items which can transact multiple items (Transaction_id, Item 1, Item 2…, Item n). In this structure, performance hours can be contracted more than the single item structures, because count can be computed by query of the input transaction tables. Our experimental results indicate that performance of the n items structure is up to 2 times better than the single item. As a result of this paper, the proposed algorithm can be applied to internet shopping, searching engine and etc.

Keywords

References

  1. Data Mining Adriaans, P.;D. Zantiage
  2. Proceeding of the 20th VLDB Conference Fast algorithms for Mining Association Rules Agrawal, R.;R. Srikant
  3. Proceeding of ACM SIGMOD Conference in Management of Data Mining association rules between sets of itrms in large databases Agrawal, R.;T. Imielinski;A. Swami
  4. Data Warehousing, Data Mining and OLAP Berson, A;S. Smith
  5. Proceeding of ACM SIGMOD Conference on Management of Data Dynamic Itemset Counting and Implication rules for market basket data Brin, S.;R. Motwani, J.D. Ullman;S. Tsur
  6. Data Mining and Knowledge Discovery v.2 no.1 Beyond Market Baskets: Generalizing Association Rules to Drpendence Rules Craig, S.;B. Sergey;M. Rajeev https://doi.org/10.1023/A:1009713703947
  7. Technical Overview : A Report on Data Mining Deker, K.M.;F. Sergio
  8. Proceedingd of 11th International Conference on Data Engineerig Set-oriented mining of association rules Houtsma, M;A. Swami
  9. Proceeding of the 22nd VLDB Conference Applying Data Mining Techniques to a Health insurance Information System Marisa, I.;S.Viveros;J.P. Nearhos;M.J. Rothman
  10. Data Mining and Knowledge Discovery v.2 no.2 An Extension to SQL for Mining Assocition Rules Meo, R.;G. Pasila;S. Ceri https://doi.org/10.1023/A:1009774406717
  11. Communications of the ACM v.39 no.11 Mining Business Databbases Ronals, J.B.;T. Khabaza
  12. Data Mining and Knowledge Discovery v.4 no.2 Integrating Assocition Rule Mining with Relational Database Systems : Alternatives and Implications Sarawagi, S.;S. Thomas;R. Agrawal https://doi.org/10.1023/A:1009887712954
  13. Proceedings of ACM SIGMOD Mining Associtions between Sets of Items in Massive Databases Srikant, R.;T. Imielinski;A. Swami
  14. Proceedings of ACM SIGMOD Mining Quantitative Association Rules in Large Relational Tables Srikant, R.;R. Agrawal
  15. Proceedings of the 4th Int'l Conference on Knowledge Discoveryj in Databases and Data Mining Mining Generalized Association rulesw and Sequential Patterns Using SQL Queries Thomas, S.;S. Sarawagi