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基于已存信息的知识发现更新方法 被引量：1

Updating Methods for Knowledge Discovering Based on Prestored Information
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摘要 在挖掘关联规则和序列模式过程中,用户往往需要多次调整最小支持度,才能获得有趣的关联规则和序列模式.现给出基于已存信息的知识发现更新方法———PSI算法和大PSI-seq算法,以提高挖掘知识的效率. In mining association rules and sequential patterns, a user may require to tune the value of the minimum support many times before a set of useful association rules and sequential patterns can be obtained from the transaction database. In order to improve the efficiency of mining knowledge, algorithm PSI and PSI - seq are given for efficient generating of new Knowledge and using restored information.
作者 王新
机构地区 云南民族大学数学与计算机科学学院
出处 《云南民族大学学报（自然科学版）》 CAS 2006年第3期238-241,共4页 Journal of Yunnan Minzu University:Natural Sciences Edition
关键词 知识发现 关联规则 序列模式 PSI算法 PSI-seq算法 knowledge discovering association rules sequential patterns PSI algorithm PSI - seq algorithm
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献5

  • 1AGRAWAL R, LMIELINSKI T, SWAMI A. Mining association rules between sets of items in large databases[C]//Proceedings of the ACM SIGMOD Conference on Management of Data. Washington D. C, 1993:207-216.
  • 2AGRAWA R, SRIKANT R, Mining sequential patterns[ C ]//Proceedings of the 11th International Conference Data Engineering, Taibei Taiwan, 1995:3 - 14.
  • 3SRIKANT R, AGRAWA R. Mining sequential patterns: Generalizations and performance improvements [ C ]//Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, 1996:3 - 17.
  • 4王新.基于覆盖运算的代表关联规则挖掘[J].计算机工程与应用,2006,42(2):176-178. 被引量：1
  • 5王新.一种基于已存信息的序列模式挖掘更新方法[J].计算机工程与应用,2006,42(4):171-173. 被引量：1

二级参考文献11

  • 1Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large databases[C].In:Proceedings of the ACM SIG- MOD Conference on Management of Data,Washington D C, 1993: 207-216.
  • 2Agrawal R, Sfikant R.Fast algorithm for mining association rules[C]. In :Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 1994 : 478-499.
  • 3Pasquier N ,Bastide Y ,Taouil R et al.Efficient mining of association rules using closed itemset lattices[J].Information Systems, 1999,24(1 ): 25 -46.
  • 4Srikant R,Agrawal R.Mining generalized association rules[J].Future Generation Computer System, 1997 ; ( 13 ) : 161 - 180.
  • 5Yugami N,Ohto Y,Okamoto S.Fast discovery of interesting rules[C]. In:Proceeding of the 4th Pacific-Asia Conference,PAKDD2000,Japan, 2000 : 17-27.
  • 6Tan P-N,Kumar V,Srivastava J.Selecting the fight objective measure for association analysis[J].Information Systems,2004, (29) :293-313.
  • 7Agrawa R, Srikant R.Mining sequential patterns[C].In : Proceedings of the 11th International Conference Data Engineering,TaipeiTaiwan, 1995:3-14.
  • 8Srikant R, Agrawa R.Mining sequential patterns : Generalizations and performance improvements[C].In:Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining,1996:3-17.
  • 9Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large databases[C].In:Proceedings of the ACM SIGMOD Conference on Management of Data.Washington D C, 1993 : 207-216.
  • 10Yugami N,Ohto Y,Okamoto S.Fast discovery of interesting rules[C]. In:Proceeding of the 4th Pacific-Asia Conference,PAKDD2000,Japan, 2000:17-27.

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云南民族大学学报（自然科学版）
2006年 第3期

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