Probabilistic Mixed Topological Map for Categorical and Continuous Data

Author

Rogovschi, Nicoleta ; Lebbah, Mustapha ; Bennani, Younés

Author_Institution

LIPN, Univ. Paris 13, Villetaneuse

fYear

2008

fDate

11-13 Dec. 2008

Firstpage

224

Lastpage

231

Abstract

This paper introduces a new probabilistic topological map as generative model that includes mixture of Gaussian and Bernoulli distribution. This model is dedicated to cluster mixed data with continuous and categorical variables. This model is fitted by maximum likelihood using the EM algorithm. Examples using real data set allow to validate our model. The proposed approach has the advantage comparing to existing topological map of providing a set of prototype with the same coding as the learning data. More information is produced with this model that could be used in practical applications.

Keywords

Gaussian distribution; data mining; learning (artificial intelligence); maximum likelihood estimation; pattern clustering; self-organising feature maps; Bernoulli distribution; Gaussian distribution; categorical data clustering; continuous data clustering; data learning; maximum likelihood algorithm; probabilistic mixed topological map; probabilistic self-organizing map; Clustering algorithms; Convergence; Gaussian distribution; Iterative algorithms; Machine learning; Prototypes; Vector quantization; Bernoulli and Gaussian mixture; EM; Self-Organizing Map; clustering; continuous and categorical data;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on

Conference_Location

San Diego, CA

Print_ISBN

978-0-7695-3495-4

Type

conf

DOI

10.1109/ICMLA.2008.13

Filename

4724979