A Novel Tree Cluster Approach Based on Least Closed Tree

The extensive application of tree model has made tree mining become a hot field in data mining research. As an important branch of tree mining, tree cluster plays a fundamental analysis role in many areas. In this paper, a tree cluster algorithm was proposed based on least closed tree, which effectively solved problems in large amount of data in practical application. The basic method is bringing forward least closed tree as the candidate cluster feature, using dynamic threshold by similarity cluster to make tree cluster operation be more quick and accurate. Experimental results show that the method has higher speed and efficiency than that of other similar ones especially when large number of tree nodes.

[1]  Yang-Yang Wu.  A Method of Discovering Relation Information from XML Data: A Method of Discovering Relation Information from XML Data , 2008 .

[2]  Chen Duan-sheng,et al.  A Method of Discovering Relation Information from XML Data , 2008 .

[3]  Xin Guo,et al.  A fast algorithm of mining induced subtrees , 2008, 2008 International Conference on Information and Automation.

[4]  Yun Chi,et al.  Indexing and mining free trees , 2003, Third IEEE International Conference on Data Mining.

[5]  Mohammad Al Hasan,et al.  ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns , 2008, Stat. Anal. Data Min..

[6]  Yun Chi,et al.  CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees , 2004, PAKDD.

[7]  George Karypis,et al.  Frequent Substructure-Based Approaches for Classifying Chemical Compounds , 2005, IEEE Trans. Knowl. Data Eng..

[8]  Mohammed J. Zaki.  Efficiently mining frequent trees in a forest: algorithms and applications , 2005, IEEE Transactions on Knowledge and Data Engineering.

[9]  Yun Chi,et al.  HybridTreeMiner: an efficient algorithm for mining frequent rooted trees and free trees using canonical forms , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[10]  Thomas Gärtner,et al.  Cyclic pattern kernels for predictive graph mining , 2004, KDD.

[11]  James W. Brown.  The ribonuclease P database , 1998, Nucleic Acids Res..

[12]  Timos K. Sellis,et al.  Clustering XML Documents Using Structural Summaries , 2004, EDBT Workshops.

[13]  Mohammad Al Hasan,et al.  An integrated, generic approach to pattern mining: data mining template library , 2008, Data Mining and Knowledge Discovery.

[14]  Charu C. Aggarwal,et al.  Xproj: a framework for projected structural clustering of xml documents , 2007, KDD '07.

[15]  Mohammad Al Hasan,et al.  ORIGAMI: Mining Representative Orthogonal Graph Patterns , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).