Title :
Multinomial Self Organizing Maps
Author :
Allouti, Faryel ; Nadif, Mohamed ; Otjacques, Benoît
Author_Institution :
LIPADE, Univ. of Paris Descartes, Paris, France
fDate :
Nov. 29 2010-Dec. 1 2010
Abstract :
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.
Keywords :
Monte Carlo methods; data visualisation; learning (artificial intelligence); matrix algebra; pattern clustering; self-organising feature maps; Monte Carlo simulations; cooccurrence data matrices; data clustering; data visualization; document clustering; learning algorithm; multinomial mixture model; multinomial self organizing maps; real datasets; Clustering; Porbabilistic formalism;
Conference_Titel :
Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-8134-7
DOI :
10.1109/ISDA.2010.5687194
