All clustering methods have to
assume some cluster relationship among the data
objects that they are applied on. Similarity
between a pair of objects can be defined either
explicitly or implicitly. In this paper, we
introduce a novel multiviewpoint-based
similarity measure and two related clustering
methods. The major difference between a
traditional dissimilarity/similarity measure and
ours is that the former uses only a single
viewpoint, which is the origin, while the latter
utilizes many different viewpoints, which are
objects assumed to not be in the same cluster
with the two objects being measured. Using
multiple viewpoints, more informative
assessment of similarity could be achieved.
Theoretical analysis and empirical study are
conducted to support this claim. Two criterion
functions for document clustering are proposed
based on this new measure. We compare them
with several well-known clustering algorithms
that use other popular similarity measures on
various document collections to verify the
advantages of our proposal.