Algorithms for clustering data
Algorithms for clustering data
Ordering effects in clustering
ML92 Proceedings of the ninth international workshop on Machine learning
ACM Computing Surveys (CSUR)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
An experimental comparison of model-based clustering methods
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Artificial Intelligence in Geography
Artificial Intelligence in Geography
Self-Organizing Maps
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On the Use of Self-Organizing Maps for Clustering and Visualization
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Robust Incremental Clustering with Bad Instance Orderings: A New Strategy
IBERAMIA '98 Proceedings of the 6th Ibero-American Conference on AI: Progress in Artificial Intelligence
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Computers & Geosciences
On the Quantization Error in SOM vs. VQ: A Critical and Systematic Study
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICICS'07 Proceedings of the 9th international conference on Information and communications security
Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps
Computers & Geosciences
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One of the most widely used clustering techniques used in GISc problems is the k-means algorithm. One of the most important issues in the correct use of k-means is the initialization procedure that ultimately determines which part of the solution space will be searched. In this paper we briefly review different initialization procedures, and propose Kohonen’s Self-Organizing Maps as the most convenient method, given the proper training parameters. Furthermore, we show that in the final stages of its training procedure the Self-Organizing Map algorithms is rigorously the same as the k-means algorithm. Thus we propose the use of Self-Organizing Maps as possible substitutes for the more classical k-means clustering algorithms.