Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results (1998)
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| Venue: | IEEE Bulletin of the Technical Committee on Data Engineering |
| Citations: | 45 - 14 self |
BibTeX
@ARTICLE{Han98hypergraphbased,
author = {Eui-Hong (Sam) Han and George Karypis and Vipin Kumar and Bamshad Mobasher},
title = {Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results},
journal = {IEEE Bulletin of the Technical Committee on Data Engineering},
year = {1998},
volume = {21}
}
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Abstract
Clustering of data in a large dimension space is of a great interest in many data mining applications. In this paper, we propose a method for clustering of data in a high dimensional space based on a hypergraph model. In this method, the relationship present in the original data in high dimensional space are mapped into a hypergraph. A hyperedge represents a relationship (affinity) among subsets of data and the weight of the hyperedge reflects the strength of this affinity. 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. We present results of experiments on two different data sets: S&P500 stock data for the period of 1994-1996 and protein coding data. These experiments demonstrate that our approach is applicable and effective in high dimensional datasets. 1 Introduction Clustering in data mining is a disco...
