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A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

MUSTAPHA LEBBAH

LIPN-UMR 7030 Université Paris 13–CNRS, 93430 Villetaneuse, France

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YOUNÈS BENNANI

LIPN-UMR 7030 Université Paris 13–CNRS, 93430 Villetaneuse, France

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NICOLETA ROGOVSCHI

LIPN-UMR 7030 Université Paris 13–CNRS, 93430 Villetaneuse, France

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https://doi.org/10.1142/S1469026808002351Cited by:11 (Source: Crossref)

Abstract

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

This is the extended version of the paper: M. Lebbah, N. Rogovschi and Y. Bennani, BeSOM: Bernoulli on Self-Organizing Map, International Joint Conferences on Neural Networks, IJCNN 2007, Orlando, Florida (12–17 August, 2007).

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