RT Journal Article
JF IEEE Transactions on Pattern Analysis & Machine Intelligence
YR 2007
VO 30
SP 62
TI LEGClust—A Clustering Algorithm Based on Layered Entropic Subgraphs
A1 Luis A. Alexandre,
A1 Jorge M. Santos,
A1 Joaquim Marques de Sa,
K1 Clustering
K1 Entropy
K1 Graphs
AB Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 0162-8828
LA English
DO 10.1109/TPAMI.2007.1142
LK http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1142