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Information Theoretic Pairwise Clustering
[chapter]
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
doi:10.1007/978-3-642-39140-8_7
fatcat:btw42jrp4jeqvaibnjw4so474y