DATA CLUSTERING USING A REORGANIZING NEURAL NETWORK
Dragan Kukolj, Branislav Atlagić, Milovan Petrov · Cybernetics & Systems · 2006
A new approach, designed for clustering of arbitrary distributed patterns, is presented. This study is concerned with the use of a self-organizing neural network as a frame for data clustering. The nearest network nodes in feature space are treated as prototypes, assigned to the corresponding cluster. The rules for dead-node shifting and simple adjustment of coordinates of the active nodes are introduced. The performance of the proposed self-organizing neural network is examined on the benchmark synthetic and the real-world problem.