Information Theoretic Pairwise Clustering [chapter]

Avishay Friedman, Jacob Goldberger
2013 Lecture Notes in Computer Science  
In this paper we develop an information-theoretic approach for pairwise clustering. The Laplacian of the pairwise similarity matrix can be used to define a Markov random walk on the data points. This view forms a probabilistic interpretation of spectral clustering methods. We utilize this probabilistic model to define a novel clustering cost function that is based on maximizing the mutual information between consecutively visited clusters of states of the Markov chain defined by the graph
more » ... ian matrix. The algorithm complexity is linear on sparse graphs. The improved performance and the reduced computational complexity of the proposed algorithm are demonstrated on several standard datasets.
doi:10.1007/978-3-642-39140-8_7 fatcat:btw42jrp4jeqvaibnjw4so474y