Multinomial Self Organizing Maps

Co-occurrence data matrices arise frequently in various important applications such as a document clustering. By considering a multinomial mixture model, we present a new probabilistic Self-Organizing Map (SOM) for clustering and visualizing this kind of data. Contrary to SOM, our proposed learning...

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Bibliographic Details
Main Authors: Allouti, F, Nadif, M, Otjacques, B
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Clustering
Clustering algorithms
Data visualization
Markov processes
Neurons
Porbabilistic formalism
Probabilistic logic
Topology
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Summary:Co-occurrence data matrices arise frequently in various important applications such as a document clustering. By considering a multinomial mixture model, we present a new probabilistic Self-Organizing Map (SOM) for clustering and visualizing this kind of data. Contrary to SOM, our proposed learning algorithm optimizes an objective function. Its performances are evaluated by using Monte Carlo simulations and real datasets.
ISSN:2164-7143
2164-7151
DOI:10.1109/ISDA.2010.5687194