New Clustering Method in High-Di mensional Space Based on Hypergraph-Models |
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Authors: | CHEN Jian-bin WANG Shu-jing and SONG Han-tao |
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Affiliation: | School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; Business College, Beijing Union University, Beijing 100025, China;China Aviation Accounting Center, Beijing 100028, China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
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Abstract: | To overcome the limitation of the traditional clustering algorithms which fail to produce meaningful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is proposed. The hypergraph model maps the relationship present in the original data in high dimensional space into a hypergraph. A hyperedge represents the similarity of attribute-value distribution between two points. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. The quality of the clustering result can be evaluated by applying the intra-cluster singularity value. Analysis and experimental results have demonstrated that this approach is applicable and effective in wide ranging scheme. |
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Keywords: | high-dimensional clustering hypergraph model data mining |
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