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Clustering Algorithm Analysis of Web Users with Dissimilarity and SOM Neural Networks

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  • Clustering
  • Dissimilarity
  • Self Organizing Feature Mapping
  • E-Commerce
  • Computer Science
  • Design


To effectively organize and analyze massive web information, design a web user’s clustering mining algorithm. SOM neural network algorithm has lots of disadvantages, to solve the data clustering, propose a new method that uses D-SOM (Dissimilarity-Self Organizing feature Mapping) algorithm, for clustering web user’s. This algorithm can estimate the center and number of clustering data set by dissimilarity computing, optimize SOM neural network learning and improve clustering effect. Through design the experiment, these web data are collected and processed by D-SOM algorithm Experimental results verify which D-SOM clustering algorithm has better clustering accuracy and imore efficient than SOM neural network algorithm.

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