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Semi Supervised Clustering: A Pareto Approach

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7376))

Abstract

In this paper we present a Pareto based multi objective algorithm for semi supervised clustering (PSC). Semi-supervised clustering uses a small amount of supervised data known as constraints, to assist unsupervised learning. Instead of modifying the clustering objective function, we add another objective function to satisfy specified constraints. We use a lexicographically ordered cluster assignment step to direct the search and a Pareto based multi objective evolutionary algorithm to maintain diversity in the population. Two objectives are considered: one that minimizes the intra cluster variance and another that minimizes the number of constraint violations. Experiments show the superiority of the method over a greedy algorithm (PCK-means) and a genetic algorithm (COP-HGA).

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Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Javid Ebrahimi & Mohammad Saniee Abadeh

Authors
  1. Javid Ebrahimi
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  2. Mohammad Saniee Abadeh
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Kohlenstraße 2, 04107, Leipzig, Germany

    Petra Perner

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© 2012 Springer-Verlag Berlin Heidelberg

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Ebrahimi, J., Saniee Abadeh, M. (2012). Semi Supervised Clustering: A Pareto Approach. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-31537-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31536-7

  • Online ISBN: 978-3-642-31537-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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