Algorithms for clustering data
Algorithms for clustering data
Self-Organizing Maps
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust growing hierarchical self organizing map
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Growing a hypercubical output space in a self-organizing feature map
IEEE Transactions on Neural Networks
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Neural maps are a very popular class of unsupervised neural networks that project high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid. It is desirable that the projection effectively preserves the structure of the data. In this paper we present a hybrid model called K-Dynamical Self Organizing Maps (KDSOM) consisting of K Self Organizing Maps with the capability of growing and interacting with each other. The input space is soft partitioned by the lattice maps. The KDSOM automatically finds its structure and learns the topology of the input space clusters. We apply our KDSOM model to three examples, two of which involve real world data obtained from a site containing benchmark data sets.