A Comparative Performance Analysis of Self Organizing Maps on Weight Initializations Using different Strategies

Abstract

Self Organizing Maps perform clustering of data based on unsupervised learning. It is of concern that initialization of the weight vector contributes significantly to the performance of SOM and since real world datasets being high-dimensional, the complexity of SOM tend to increase tremendously leading to increased time consumption as well. Our work focuses… (More)

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