Self-organizing Map with Modified Self Organizing Map Clustering
Keywords:
Clustering Algorithms, Learning rate, Weight vector, SOM, Modified SOMAbstract
Clustering is a very well-known technique of data mining which is mostly used method of analyzing and describing the data. It is one of the techniques to deal with the large geographical datasets. Clustering is the mostly used method of data mining. KohonenSOM is a classical method for clustering. In this paper, a new approach is proposed by combining neural network and clustering algorithms. We propose a modified Self Organizing Map algorithm which initially starts with null network and grows with the original data space as initial weight vector, updating neighbourhood rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment of simple SOM. In this paper, existing SOM and modified SOM have been compared by using different parameters.
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