ISSN : 1796-217X
Volume : 4    Issue : 4    Date : June 2009

RCHIG: An Effective Clustering Algorithm with Ranking
Jianwen Tao
Page(s): 382-389
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In this paper, we address the problem of generating clusters for a specified type of objects, as well
as ranking information for all types of objects based on these clusters in a heterogeneous
information graph. A novel clustering framework called RCHIG is proposed that directly generates
clusters integrated with ranking. Based on initial K clusters, ranking is applied separately, which
serves as a good measure for each cluster. Then, we use a mixture model to decompose each
object into a K-dimensional vector, where each dimension is a component coefficient with respect
to a cluster, which is measured by rank distribution. Objects then are reassigned to the nearest
cluster under the new measure space to improve clustering. As a result, quality of clustering and
ranking are mutually enhanced, which means that the clusters are getting more accurate and the
ranking is getting more meaningful. Such a progressive refinement process iterates until little
change can be made. Our experiment results show that RCHIG can generate more accurate
clusters and in a more efficient way than the state-of-the-art link-based clustering methods.
Moreover, the clustering results with ranks can provide more informative views of data compared
with traditional clustering.

Index Terms
Binary Information Graph, Clustering, K-dimensional Vector, Link-based Clustering