• Corpus ID: 18458217

Topological map for binary data

@inproceedings{Lebbah2000TopologicalMF,
  title={Topological map for binary data},
  author={Mustapha Lebbah and Fouad Badran and Sylvie Thiria},
  booktitle={ESANN},
  year={2000}
}
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. 

Figures and Tables from this paper

Categorical Topological Map
TLDR
This paper proposes a probabilistic formalism where the neurons now represent probability tables in topological maps, and shows the good quality of the topological order obtained as well as its performances in classification.
Mixed Topological Map
TLDR
A new algorithm based on a topological map model and dedicated to mixed data, with numerical and binary components, is proposed which computes directly the referent vectors, as mixed data vectors sharing the same interpretation with the observations.
Probabilistic Mixed Topological Map for Categorical and Continuous Data
TLDR
A new probabilistic topological map as generative model that includes mixture of Gaussian and Bernoulli distribution is introduced that is dedicated to cluster mixed data with continuous and categorical variables.
Relational topological clustering
TLDR
An hybrid algorithm is proposed, which deals linearly with large datasets, provides a natural clusters identification and allows a visualization of the clustering result on a two dimensional grid while preserving the a priori topological order of the data.
Weighted Topological Clustering for Categorical Data
TLDR
A probabilistic self-organizing map for topographic clustering, analysis of categorical data, and the proposed learning algorithm optimizes an objective function is introduced.
Relational Topological Map
TLDR
A hybrid algorithm, which deals linearly with large data sets, provides a natural clusters identification and allows a visualization of the clustering result on a two-dimensional grid while preserving the a priori topological order of this data.
Classification relationnelle topographique
TLDR
An hybrid algorithm is proposed, which deals linearly with large data sets, provides a natural clusters identification and allows a visualization of the clustering result on a two dimensional grid while preserving the a priori topological order of the data.
Probabilistic Topological Map and Binary data
TLDR
This paper adapts the Bernoulli mixture approach to the model of binary topological map and shows that using a probabilistic formalism gives rise to better quantization process and classification performances.
A Probabilistic Self-Organizing Map for Binary Data Topographic Clustering
TLDR
A probabilistic self-organizing map for topographic clustering, analysis and visualization of multivariate binary data or categorical data using binary coding using Bernoulli distribution is introduced.
Binary-based similarity measures for categorical data and their application in Self- Organizing Maps
TLDR
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.
...
...

References

SHOWING 1-10 OF 15 REFERENCES
The self-organizing map
Self-Organizing Map. Springer, Berlin.(1994)
  • 1994
Contribution a la classi cation de donn ees binaires et qualitatives, th ese de l'universit e de Metz.(1989)
  • 1989
Clustering AnalysisAn Introduction to Symbolic Data
  • Digital pattern recognition
  • 1996
Analysis, in :K.S. Fu(ED), Digital pattern recognition, Springer,New York.An Introduction to Symbolic Data
  • 1996
La r egression PLS, th eorie et pratique
  • Edition Technip
  • 1998
Contribution a la classiication de donn ees binaires et qualitatives , t h ese de l'universit e de Metz
  • Contribution a la classiication de donn ees binaires et qualitatives , t h ese de l'universit e de Metz
  • 1989
Competitive Learning for Binary Data
  • Proc of ICANN'98,
  • 1998
...
...