Title :
Probabilistic Mixed Topological Map for Categorical and Continuous Data
Author :
Rogovschi, Nicoleta ; Lebbah, Mustapha ; Bennani, Younés
Author_Institution :
LIPN, Univ. Paris 13, Villetaneuse
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;
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
DOI :
10.1109/ICMLA.2008.13
