Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
GTM: the generative topographic mapping
Neural Computation
Self-Organizing Maps
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
MACLAW: A modular approach for clustering with local attribute weighting
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
Fuzzy K-Means with Variable Weighting in High Dimensional Data Analysis
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Self-organizing mixture models
Neurocomputing
A novel fuzzy c-means clustering algorithm
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Unsupervised feature selection for text data
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A graph based framework for clustering and characterization of SOM
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Simultaneous pattern and variable weighting during topological clustering
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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We introduce a new learning approach, which provides simultaneously Self-Organizing Map (SOM) and local weight vector for each cluster. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector). Based on the Self-Organizing Map approach, we present two new simultaneously clustering and weighting algorithms: local weighting observation lwo-SOM and local weighting distance lwd-SOM. Both algorithms achieve the same goal by minimizing different cost functions. After learning phase, a selection method with weight vectors is used to prune the irrelevant variables and thus we can characterize the clusters. We illustrate the performance of the proposed approach using different data sets. A number of synthetic and real data are experimented on to show the benefits of the proposed local weighting using self-organizing models.