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Supervised and Unsupervised Clustering with Probabilistic Shift

  • Sanketh Shetty
  • Narendra Ahuja
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

We present a novel scale adaptive, nonparametric approach to clustering point patterns. Clusters are detected by moving all points to their cluster cores using shift vectors. First, we propose a novel scale selection criterion based on local density isotropy which determines the neighborhoods over which the shift vectors are computed. We then construct a directed graph induced by these shift vectors. Clustering is obtained by simulating random walks on this digraph. We also examine the spectral properties of a similarity matrix obtained from the directed graph to obtain a K-way partitioning of the data. Additionally, we use the eigenvector alignment algorithm of [1] to automatically determine the number of clusters in the dataset. We also compare our approach with supervised[2] and completely unsupervised spectral clustering[1], normalized cuts[3], K-Means, and adaptive bandwidth meanshift[4] on MNIST digits, USPS digits and UCI machine learning data.

Keywords

Spectral Cluster Transition Probability Matrix Unsupervised Cluster Cluster Core Shift Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sanketh Shetty
    • 1
  • Narendra Ahuja
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of IllinoisUrbanaUSA

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