Clustering in a High-Dimensional Space Using Hypergraph Models (1997)
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@MISC{Han97clusteringin,
author = {Eui-Hong (Sam) Han and George Karypis and Vipin Kumar and Bamshad Mobasher},
title = {Clustering in a High-Dimensional Space Using Hypergraph Models },
year = {1997}
}
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Abstract
Clustering of data in a large dimension space is of a great interest in many data mining applications. Most of the traditional algorithms such as K-means or AutoClass fail to produce meaningful clusters in such data sets even when they are used with well known dimensionality reduction techniques such as Principal Component Analysis and Latent Semantic Indexing. In this paper, we propose a method for clustering of data in a high dimensional space based on a hypergraph model. The hypergraph model maps the relationship present in the original data in high dimensional space 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 ...
