论文信息 - An Effective Hypergraph Clustering in Multi-Stage Data Mining of Traditional Chinese Medicine Syndrome Differentiation

An Effective Hypergraph Clustering in Multi-Stage Data Mining of Traditional Chinese Medicine Syndrome Differentiation

Traditional Chinese medicine is mysterious for its special diagnosis and treatment. In TCM, syndrome differentiation is the method of recognizing and diagnosing diseases or body imbalances in TCM. In this paper, we first give a hierarch model of differentiation syndrome in traditional Chinese medicine according to the model data mining procedure is designed to complete it. Given special data mining schema and character of high-dimensional data sets, we introduce hypergraph based on greedy algorithm in cluster and similarity measure during clustering stage. Finally, the experiment shows that the hypergraph clustering is correct and efficient, which in return could be important for association rules and diagnosis

Bo Wang | Mingwei Zhang | Bin Zhang | Wei-Jie Wei

[1] G. Karypis,et al. Clustering In A High-Dimensional Space Using Hypergraph Models , 2004 .

[2] Tian Zhang,et al. BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[3] Jiong Yang,et al. STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[4] Daniel A. Keim,et al. An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.

[5] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[6] Padhraic Smyth,et al. Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.

[7] Dimitrios Gunopulos,et al. Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[8] Jiawei Han,et al. Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.

[9] Aidong Zhang,et al. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases , 1998, VLDB.