Self-organizing maps
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Multivariate Descriptive Statistical Analysis
Multivariate Descriptive Statistical Analysis
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
SOM-based algorithms for qualitative variables
Neural Networks - 2004 Special issue: New developments in self-organizing systems
| Hi-index | 0.01 |
The Kohonen algorithm (SOM, Self-Organization and Associative Memory, Springer Series in Information Sciences, vol. 8, Springer, Berlin, 1984; Self-Organizing Maps, Springer Series in Information Science, vol. 30, Springer, Berlin, 1995) is a very powerful tool for data analysis. It was originally designed to model organized connections between some biological neural networks. It was also immediately considered as a very good algorithm to realize vectorial quantization, and at the same time pertinent classification, with nice properties for visualization. If the individuals are described by quantitative variables (ratios, frequencies, measurements, amounts, etc.), the straightforward application of the original algorithm leads to build code vectors and to associate to each of them the class of all the individuals which are more similar to this code-vector than to the others. But, in case of individuals described by categorical (qualitative) variables having a finite number of modalities (like in a survey), it is necessary to define a specific algorithm. In this paper, we present a new algorithm inspired by the SOM algorithm, which provides a simultaneous classification of the individuals and of their modalities.