데이터 마이닝의 유용성

  • Published : 1998.09.01

Abstract

Keywords

References

  1. Proc. ACM SIGMOD Mining Association Rules between Sets of Items in Large Databases R.Agrawal;T.Imielinske;A.Swami
  2. IEEE Transactions on Knowledge and Data Engineering, Special issue on Learning and Discovery in Knowledge-Based Databases v.5 no.6 Database Mining:A Performance Perspective R.Agrawal;T.Imielinski;A.Swami
  3. Quasi-Cubes:A Space-Efficient Way to Support Approximate D.Barbara;M.Sullivan
  4. 13th Annual ACM Symposium on Computational Geometry Nice Time-Series Similarity Problems and Well-Separated Geometric Sets B.Bollobas;G.Das;D.Gunopulos;H.Mannila
  5. Technical Report MP-TR-98-01 Data Mining:Overview and Optimization Opportunities P.S.Bradley;U.M.Fayyad;O.L.Mangasarian
  6. Proc. SIGMOD Dynamic Itemset Counting and Implication Rules for Market Basket Data S.Brin;R.Motwami;J.Ullman;S.Tsur
  7. IEEE Tran. Knowledge and Data Engineering v.8 no.6 Data Mining:An Overview from a Database Perspective M.S.Chen;J.Han;P.S.Yu
  8. PKDD'97 Finding Similar Time Series G.Das;D.Gunopulos;H.Mannila
  9. Comm. of the ACM v.39 no.11 The KDD process for Extracting Useful Knowledge from Volumes of Data U.M.Fayyad;G.Piatetsky-Shapiro;P.Smyth
  10. Advanced in Knowledge Discovery and Data Mining U.M.Fayyad;G.Piatetsky-Shapiro;P.Smyth;R.Uthurusamy
  11. Data Mining and Knowledge Discovery v.1 no.1 Data Cube, A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals J.Gray;S.Chaudhuri;A.Bosworth;A.Layman;D.Reichart;M.Venkatrao;F.Pellow;Pirahesh
  12. Data Mining and Knowledge Discovery v.1 no.1 Bayesian Networks for Data Mining D.Heckerman
  13. Technical Report CSD-98-1004 Online Association Rule Mining C.Hidber
  14. Data Mining in Large Databases Using Domain Generalization Graphs R.J.Hilderman;H.J.Hamilton;N.Cercone
  15. Report CS-R9406 Data Mining:The Search for Knowledge in Databases M.Holsheimer;A.Siebes
  16. Technical Report CS-TR-94-1527, Univ. of California From Knowledge to Belief D.Koller
  17. Technical Report CS-TR-97, George Mason University MTLS:A Tool for Extending and Refining Knowledge Bases O.Lee
  18. AAAI Workshop on Knowledge Discovery in Databases Efficient Algorithms for Discovering Association Rules H.Mannila;H.Toivonen;I.Verkamo;Usama M. Fayyad(ed.);Ramasamy Uthurusamy(ed.)
  19. EDBT SLIQ:A Fast Salable Classifier for Data Mining M.Mehta;R.Agrawal;J.Rissanen
  20. 4th Int. Conf. on Knowledge Discovery and Data Mining New York Evaluating Usefulness for Dynamic Classification G.Nakhaeizadeh;C.Taylor;C.Lanquillion
  21. 4th Int. Conf. on Knowledge Discovery and Data Mining New York A Belief-Driven Method for Discovering Unexpected Pattern B.Padmanabhan;A.Tuzhilin
  22. Proc. ACM SIGMOD Mining Quantitative Association Rules in Large Relational Tables R.Srikant;R.Agrawal
  23. 4th Int. Conf. on Knowledge Discovery and Data Mining New York Interestingness-based Interval Merger for Numeric Association rules K.Wang;S.H. William Tay;B.Liu
  24. 1st Int'l Conference on Discovery Science, Fukuoka, Japan Query-Initiated Discovery of Interesting Association Rules J.Yoon;L.Kerschberg
  25. Data Clustering for Very Large Datasets Plus Applications T.Zhang