BeSOM : Bernoulli on self-organizing map

被引：6

作者：

Lebbah, Mustapha ^{[1
]}

Rogovschi, Nicoleta ^{[2
]}

Bennani, Younes ^{[2
]}

机构：

[1] Univ Paris 13, LIM&BIO Lab, UFR Sante, Med & Biol Humaine SMBH, Leonard Vinci 74,Rue Marcel Cachin, F-93017 Bobigny, France

[2] Univ Paris 13, LIPN Lab, F-93430 Villetaneuse, France

来源：

2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 | 2007年

关键词：

D O I：

10.1109/IJCNN.2007.4371030

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces a probabilistic self-organizing map for clustering, analysis and visualization of multivariate binary data. 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, BeSOM, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with two data sets taken from a public data set repository: a handwritten digit data set and a zoo data set. The results show a good quality of the topological ordering and homogenous clustering.

引用

页码：631 / +

页数：2