期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种改进的多值属性模式聚类算法

An Improved Pattern Cluster Algorithm for Quantitative Attributes
下载PDF
导出
摘要 pCluster算法是面向多值属性数据的聚类算法,能识别出多值属性间的相似性。针对模式聚类算法pCluster效率低的问题,提出pCluster的改进算法。实验证明,该改进算法能更高效地获得预期聚类结果。 pCluster is a kind of cluster algorithm for the numeric attributes, which could identify the similar trend among several attributes, but it is less than satisfactory in algorithm efficiency. In this paper, an improved p Cluster algorithm was presented, the expected cluster results could be produced more efficient.
作者 王珊珊 梁同乐
机构地区 广东轻工职业技术学院 广东邮电职业技术学院
出处 《自动化与信息工程》 2015年第5期33-39,共7页 Automation & Information Engineering
关键词 关联规则 多值属性 模式聚类 Association Rule Quantitative Attribute Pattern Cluster
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献19

  • 1Srikant R, Agrawal R. Mining quantitative association rules in large relational tables[C]. Montreal: in Proc. of the ACM SIGMOD International Conference on Management of Data, 1996, 25: 1-12.
  • 2Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases[C]. Washington: in Proc. of the ACM SIGMOD International Conference on Management of Data, 1993, 22:207-216.
  • 3李国雁,沈夏炯.一种基于矩阵的多值关联规则的挖掘算法[J].计算机工程与科学,2008,30(5):72-74. 被引量：7
  • 4贺志,田盛丰,黄厚宽.一种挖掘数值属性的二维优化关联规则方法(英文)[J].软件学报,2007,18(10):2528-2537. 被引量：5
  • 5陈文.基于位矩阵的加权频繁k项集生成算法[J].计算机工程,2010,36(5):54-56. 被引量：6
  • 6姜丽莉,孟凡荣,周勇.多值属性关联规则挖掘的Q-Apriori算法[J].计算机工程,2011,37(9):81-83. 被引量：9
  • 7Donoho David L. High-dimentional data analysis: The curses and blessings ofdimensionality [R]. Los Angeles, 2000.
  • 8Beyer K, Goldstein J, Ramakrishnan R, et al. When is nearest neighbors meaningful[C]. Jerusalem: in Proc. of the Intema- tional Conference Database Theories, 1999:217-235.
  • 9Agrawal R, Gehrke J, Gunopulos D, et al. Authomatic subspace clustering of high dimensional data for data mining applica- tions[C]. New York: Proc of ACM SIGMOD Intemational Conference on Management of Data, 1998, 27: 94-105.
  • 10Aggarwal C C, Yu P S. Finding generalized projected clusters in high dimensional spaces[C]. Dallas: Proc of ACM SIG- MOD International Conference on Management of Data, 2000, 29: 70-81.

二级参考文献44

  • 1胡慧蓉,王周敬.一种基于关系矩阵的关联规则快速挖掘算法[J].计算机应用,2005,25(7):1577-1579. 被引量：21
  • 2曾万聃,周绪波,戴勃,常桂然,李春平.关联规则挖掘的矩阵算法[J].计算机工程,2006,32(2):45-47. 被引量：33
  • 3杨炳儒 孙海洪.语言场理论在挖掘关联规则中的应用[J].计算机科学,2000,27(11):66-69.
  • 4Cai C H, Fu A W C, Cheng C H, et al. Mining Association Rules with Weighted Items[C]//Proceedings of the International Database Engineering and Applications Symposium. [S. l.]: IEEE Computer Society, 1998: 68-77.
  • 5Hinneburg A and Keim D A. An efficient approach to clustering in large multimedia databases with noise [C]. Processing of the 4th International Conference on Knowledge Discovery and Data Mining, New York: AAAI Press, 1998:58-68.
  • 6Hinneburg A and Gabriel H H. DENCLUS2.0: Fast Clustering based on kernel density estimation[C]. IDA, 2007, LNCS 4723: 70-80.
  • 7Vineet C J, Mohammad A H, and Saeed S, et al.. SPARCL: Efficient and effective shape-based clustering[C]. Proceedings of 8th IEEE International Conference on Data Mining, Pisa, Italy, 2008: 93-102.
  • 8Tao P, Ajay J, and David J H, et al.. DECODE: A new method for discovering clusters of different densities in spatial data [J]. Data Mining and Knowledge Discovery, 2009, 18(3): 337-369.
  • 9Hans H P, Peer K, and Arthir Z. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering [J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2009, 3(1): 1-58.
  • 10Ng K, Fu A, and Wong C W. Projective clustering by histograms [J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(3): 369-383.

共引文献19

自动化与信息工程
2015年 第5期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部