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Proceedings Paper

High dimensional data clustering by partitioning the hypergraphs using dense subgraph partition
Author(s): Xili Sun; Shoucai Tian; Yonggang Lu
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Paper Abstract

Due to the curse of dimensionality, traditional clustering methods usually fail to produce meaningful results for the high dimensional data. Hypergraph partition is believed to be a promising method for dealing with this challenge. In this paper, we first construct a graph G from the data by defining an adjacency relationship between the data points using Shared Reverse k Nearest Neighbors (SRNN). Then a hypergraph is created from the graph G by defining the hyperedges to be all the maximal cliques in the graph G. After the hypergraph is produced, a powerful hypergraph partitioning method called dense subgraph partition (DSP) combined with the k-medoids method is used to produce the final clustering results. The proposed method is evaluated on several real high-dimensional datasets, and the experimental results show that the proposed method can improve the clustering results of the high dimensional data compared with applying k-medoids method directly on the original data.

Paper Details

Date Published: 14 December 2015
PDF: 9 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130B (14 December 2015); doi: 10.1117/12.2205743
Show Author Affiliations
Xili Sun, Lanzhou Univ. (China)
Shoucai Tian, Lanzhou Univ. (China)
Yonggang Lu, Lanzhou Univ. (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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