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面向对象数据库中的频繁模式发现

Frequent pattern discovery in object-oriented databases
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摘要 提出了基于高阶归纳逻辑编程发现面向对象数据库中的频繁模式的算法。此算法使用高阶逻辑编程语言Escher作为数据及模式表示语言。由于高阶逻辑编程语言不仅能描述复杂结构的数据而且还能描述复杂的模式，以及Escher语言的强类型语法能有效缩小由于高阶逻辑编程语言较强表达能力所带来的较大的模式搜索空间，故此算法能充分利用面向对象数据库中丰富的语义信息引导频繁模式搜索过程且能发现复杂频繁模式。实验证明，此算法在效率和发现的频繁模式质量上都优于经典的WARMAR算法。 The paper presents an algorithm for discovering frequent patterns in object-oriented databases based on higher-order inductive logic programming. The algorithm adopts the higher-order logic programming language Escher to represent data and patterns. As the higher-order logic programming language not only can describe complex structured data but also can represent complex pattems, and the typed syntax of Escher can effectively reduce the huge search space of patterns caused by the stronger representation ability of the higher-order logic programming language, the proposed algorithm can take full advantage of rich semantic information of object-oriented databases to guide the process of search and find complex frequent patterns. The experiments show that the algorithm is superior to the WARMAR （a typical algorithm） in both the quality of frequent patterns and the efficiency.
作者 李琳娜 张志平
机构地区 中国科学技术信息研究所
出处 《高技术通讯》 CAS CSCD 北大核心 2011年第1期15-21,共7页 Chinese High Technology Letters
基金 国家自然科学基金（60875029）资助项目.
关键词 面向对象数据库 数据挖掘 频繁模式发现 高阶归纳逻辑编程 object-oriented database, data mining, frequent pattern discovery, higher-order inductive logic programming
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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  • 1Carey M J, DeWitt D J. Of objects and databases: a decade of turmoil. In: Proceedings of the 22th International Conference on Very Large Data Bases. San Francisco, USA, 1996. 3-14.
  • 2Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases. San Francisco, USA, 1994. 487-499.
  • 3Hipp J, Guntzer U, Nakaeizadeh G. Algorithms for association rule mining: a general survey and comparison. ACM SIGKDD Explorations, 2000, 2(1) : 58-64.
  • 4Nijssen S, Kok J N. Faster association rules for multiple relations. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence. Seattle, USA, 2001. 891-896.
  • 5Bowers A F, Giraud-Carrier C, Lloyd J W. Classification of individuals with complex structure. In: Proceedings of the 17th International Conference on Machine Learning. Standord, USA, 2000. 81-88.
  • 6Lloyd J W. Logic for Learning: Knowledge Representation, Computation and Learning in Higher-order Logic. Berlin and Heidelberg: Springer-Verlag, 2002.
  • 7Lloyd J W, Mendelson S, Smola A J, et al. Learning Comprehensible Theories from Structured Data. New York: Springer-Verlag, 2003. 203-225.
  • 8Kuba P, Popelinsky L. Mining frequent patterns in object-oriented data. In: Proceedings of the 2nd International Workshop on Mining Graphs, Trees and Sequences, Pisa, Italy, 2004. 15-25.
  • 9Manrfila H, Toivonen H. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1997, 1(3): 241-258.
  • 10Dehaspe L, Raedt L D. Mining association rules in multiple relations. In: Proceedings of the 7th International Workshop on Inductive Logic Programming. Prague, Czech Republic, 1997. 251-264.
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2011年 第1期

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