Web clustering using a two-layer approach

  • Authors:

  • Yanping Li;Jinsheng Xing;Rui Wu;Fulan Zheng

  • Affiliations:

  • School of Mathematics and Computer, Shanxi Normal University, Linfen, Shanxi, China;School of Mathematics and Computer, Shanxi Normal University, Linfen, Shanxi, China;School of Mathematics and Computer, Shanxi Normal University, Linfen, Shanxi, China;School of Mathematics and Computer, Shanxi Normal University, Linfen, Shanxi, China

  • Venue:
  • WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
  • Year:
  • 2011

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Abstract

Internet is a rich and potential information base. It needs scientific and effective methods in order to find interesting information. Researchers have proposed many web clustering algorithms, but it spends too much time using a simple kind of clustering algorithms, because the number of the web information is huge. Considering the efficiency and the effect of the clustering, in the paper, we use a two-layer web clustering approach to cluster for a number of web access patterns from web logs. At the first layer, we use the LVQ (Learning Vector Quantization) neural network to group the web access patterns to several representative clustering centers. At the second layer, the rough k-means algorithm is adopted to deal with the result of the first layer, producing the final classifications. The experimental results show that the effect is close to monolayer clustering algorithm the rough k-means, and the efficiency is better than the rough k-means by using the two-layer web clustering approach.