A Dynamic SOM Algorithm for Clustering Large-Scale Document Collection

A dynamic SOM algorithm of incremental gradient descent to cluster large-scale document collection is proposed in this paper. In comparison with other SOM algorithms (e.g. GHSOM), the size of output layer in our algorithm can be gradually reduced and dynamically by inserting suitable number of neurons, thus the number of underutilized neurons can be reduced greatly and the training results of this algorithm can fully represent the distribution of topics in document collection. In addition, when using this algorithm to cluster large-scale documents the computation cost can also be shortened remarkably. The overused neurons have been split again to optimize the cluster results further. A good result of cluster can be gained. Experiments results proved the effectiveness of this algorithm.