Algorithms for clustering data
Algorithms for clustering data
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
A Bayesian analysis of self-organizing maps
Neural Computation
A multiple cause mixture model for unsupervised learning
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Clustering Large Categorical Data
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Speed-up for the expectation-maximization algorithm for clustering categorical data
Journal of Global Optimization
Bi-level clustering of mixed categorical and numerical biomedical data
International Journal of Data Mining and Bioinformatics
Computation of initial modes for K-modes clustering algorithm using evidence accumulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Self-organizing mixture models
Neurocomputing
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper introduces a probabilistic self-organizing map for topographic clustering, analysis of categorical data. By considering a parsimonious mixture model, we present a new probabilistic Self-Organizing Map (SOM). The estimation of parameters is performed by the EM algorithm. Contrary to SOM, our proposed learning algorithm optimizes an objective function. Its performance is evaluated on real datasets.