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14 December 2015 High dimensional data clustering by partitioning the hypergraphs using dense subgraph partition
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130B (2015) https://doi.org/10.1117/12.2205743
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xili Sun, Shoucai Tian, and Yonggang Lu "High dimensional data clustering by partitioning the hypergraphs using dense subgraph partition", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130B (14 December 2015); https://doi.org/10.1117/12.2205743
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