Efficient Robbins–Monro procedure for multivariate binary data

2018 ◽  
Vol 2 (2) ◽  
pp. 172-180 ◽  
Author(s):  
Cui Xiong ◽  
Jin Xu
Keyword(s):  
Binary Data ◽  
Multivariate Binary Data

Conditional independence of multivariate binary data with an application in caries research

2007 ◽  
Vol 51 (6) ◽  
pp. 3223-3234 ◽  
Author(s):  
María José García-Zattera ◽  
Alejandro Jara ◽  
Emmanuel Lesaffre ◽  
Dominique Declerck
Keyword(s):  
Conditional Independence ◽  
Binary Data ◽  
Multivariate Binary Data

An Extension of Yule's Q to Multivariate Binary Data

Biometrics ◽  
1994 ◽  
Vol 50 (3) ◽  
pp. 847 ◽  
Author(s):  
Stuart R. Lipsitz ◽  
Garrett Fitzmaurice
Keyword(s):  
Binary Data ◽  
Multivariate Binary Data

A PROBABILISTIC SELF-ORGANIZING MAP FOR BINARY DATA TOPOGRAPHIC CLUSTERING

2008 ◽  
Vol 07 (04) ◽  
pp. 363-383 ◽  
Author(s):  
MUSTAPHA LEBBAH ◽  
YOUNÈS BENNANI ◽  
NICOLETA ROGOVSCHI
Keyword(s):  
Binary Data ◽  
Learning Algorithm ◽  
Data Sets ◽  
Self Organizing Map ◽  
Data Set ◽  
Binary Coding ◽  
Public Data ◽  
Multivariate Binary Data ◽  
Self Organizing

This paper introduces a probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding. 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, Bernoulli on self-organizing map, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with six data sets taken from a public data set repository. The results show a good quality of the topological ordering and homogenous clustering.

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