Open AccessProceedings Article
Topological map for binary data.
Moustapha Lebbah,Fouad Badran,Sylvie Thiria +2 more
- 01 Jan 2000
pp 267-272
33
TL;DR: The eficiency of the proposed method when applied to high-dimensinal binary data is shown, which takes into account possible asymmetries of binary data.
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Abstract: We propose a new algorithm using topological map on binary
data. The usual Euclidean distance is replaced by binary distance
measures, which take into account possible asymmetries of binary
data. The method is illustrated on an example taken from literature.
Finally an application from chemistry is presented. We show the
eficiency of the proposed method when applied to high-dimensinal
binary data.
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Citations
The Self-Organizing Map
Teuvo Kohonen
- 01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
2.9K
A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.
TL;DR: Simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data found that the 3 approaches can exhibit profound differences when applied to real data.
Patent
Methods involving artificial intelligence
Olivier De Lacharriere,Philippe Bastien,Fouad Badran,Sylvie Thiria +3 more
- 27 May 2003
TL;DR: In this article, a dynamic cluster method, mobile center method, and/or a k-means algorithm is used to generate a profile data set using neighborhood data, where data is accessed and accessed data is processed using a dynamic clustering method.
78
Binary-based similarity measures for categorical data and their application in Self- Organizing Maps
Fernando C. Lourenço,Victor Lobo,Fernando Bacao,Gestão de Informação +3 more
- 01 Jan 2004
TL;DR: Some of the most common binary-based similarity measures that can be applied to high dimensional data are reviewed and evaluated empirically using the Self-Organizing Maps (SOM) algorithm.
Self-Organizing Map and clustering algorithms for the analysis of occupational accident databases
TL;DR: A two-level approach based on the joint use of the Kohonen's Self-Organizing Map and the k-means clustering algorithm to discover the most common sequences of events leading to accidents for devising preventive actions in occupational accident databases.
49
References
The Self-Organizing Map
Teuvo Kohonen
- 01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
2.9K
•Proceedings Article
Multiple correspondence analysis of a crosstabulations matrix using the Kohonen algorithm.
Smaïl Ibbou,Marie Cottrell +1 more
- 01 Jan 1995
36
Probabilistic self-organizing map and radial basis function networks
TL;DR: A new learning algorithm probabilistic self-organizing map (PRSOM) using a Probabilistic formalism for topological maps that approximates the density distribution of the input set with a mixture of normal distributions is proposed.
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