Algorithms for clustering data
Algorithms for clustering data
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
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
From variable weighting to cluster characterization in topographic unsupervised learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Self-organizing mixture models
Neurocomputing
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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This paper addresses the problem of detecting a subset of the most relevant features and observations from a dataset through a local weighted learning paradigm. We introduce a new learning approach, which provides simultaneously Self-Organizing Map (SOM) and double local weighting. The proposed approach is computationally simple, and learns a different features vector weights for each cell (relevance vector) and an observation weighting matrix. Based on the lwo-SOM and lwd-SOM [7], we present a new weighting approach allowing to take into account the importance of the observations and of the variables simultaneously called dlw-SOM. After the learning phase, a selection method is used with weight vectors to prune the irrelevant variables and thus we can characterize the clusters. A number of synthetic and real data are experimented on to show the benefits of the proposed double local weighting using self-organizing models.