期刊文献+

基于频繁模式树的AOI聚类算法

The AOI Clustering Algorithm Based on Frequent Pattern Tree
下载PDF
导出
摘要 为了克服KM-AOI算法聚类效率较低的缺点,提出了基于频繁模式树的AOI聚类算法,即在聚类过程中借助频繁模式树,采取分而治之的策略处理警报集以得到规则。然后举例说明了利用该算法进行聚类的过程。实例表明,该算法能够明显提高聚类的效率。 In order to overcome the disadvantage of low efficiency of KM-AOI algorithm,an AOI clustering algorithm based on frequent pattern tree is presented.Using frequent pattern tree,it divides and rules the alarms during the clustering process.Then an example is included in this paper to demonstrate the new clustering process,which shows practically that this algorithm can enhance the efficiency of clustering apparently.
出处 《计算机工程与应用》 CSCD 北大核心 2004年第35期178-179,217,共3页 Computer Engineering and Applications
基金 山东省基金项目(编号:003090309)资助
关键词 AOI方法 频繁模式树 聚类算法 入侵检测 clustering,Attribute Oriented Induction,frequent pattern tree
  • 相关文献

参考文献5

二级参考文献20

  • 1[1]Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. In: Beeri C, et al, eds. Proc. of the 7th Int'l. Conf. on Database Theory. Jerusalem: Springer-Verlag, 1999. 398~416.
  • 2[2]Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Beeri C, et al, eds. Proc. of the 20th Int'l. Conf. on Very Large Databases. Santiago: Morgan Kaufmann Publishers, 1994. 487~499.
  • 3[3]Pei J, Han J, Mao R. CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Gunopulos D, et al, eds. Proc. of the 2000 ACM SIGMOD Int'l. Workshop on Data Mining and Knowledge Discovery. Dallas: ACM Press, 2000. 21~30.
  • 4[4]Burdick D, Calimlim M, Gehrke J. MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Georgakopoulos D, et al, eds. Proc. of the 17th Int'l. Conf. on Data Engineering. Heidelberg: IEEE Press, 2001. 443~452.
  • 5[5]Zaki MJ, Hsiao CJ. CHARM: An efficient algorithm for closed itemset mining. In: Grossman R, et al, eds. Proc. of the 2nd SIAM Int'l. Conf. on Data Mining. Arlington: SIAM, 2002. 12~28.
  • 6[6]Liu JQ, Pan YH, Wang K, Han J. Mining frequent item sets by opportunistic projection. In: Hand D, et al, eds. Proc. of the 8th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining. Alberta: ACM Press, 2002. 229~238.
  • 7[7]Srikant R. Quest synthetic data generation code. San Jose: IBM Almaden Research Center, 1994. http://www.almaden.ibm.com/ software/quest/Resources/index.shtml
  • 8[8]Blake C, Merz C. UCI Repository of machine learning. Irvine: University of California, Department of Information and Computer Science, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html
  • 9Sheikholeslami G, Chatterjee S, Zhang A. Wave-Cluster: A multi-resolution clustering approach for very large spatial databases. In:Proceedings of the 24th International Conference on Very Large Databases. New York, 1998. 428~439.
  • 10Aggrawal R, Gehrke J, Gunopulos D, Raghawan P. Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle, WA, 1998.94~ 105.

共引文献195

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