1 research outputs found
Algorithms and Complexity of Range Clustering
We introduce a novel criterion in clustering that seeks clusters with limited
range of values associated with each cluster's elements. In clustering or
classification the objective is to partition a set of objects into subsets,
called clusters or classes, consisting of similar objects so that different
clusters are as dissimilar as possible. We propose a number of objective
functions that employ the range of the clusters as part of the objective
function. Several of the proposed objectives mimic objectives based on sums of
similarities. These objective functions are motivated by image segmentation
problems, where the diameter, or range of values associated with objects in
each cluster, should be small. It is demonstrated that range-based problems are
in general easier, in terms of their complexity, than the analogous
similarity-sum problems. Several of the problems we present could therefore be
viable alternatives to existing clustering problems which are NP-hard, offering
the advantage of efficient algorithms.Comment: Submitte