A Kind of Hierarchical K-Means Web Log Clustering Algorithm

Abstract:

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Clustering techniques are often used in Web log mining to analyze user’s interest on the web pages. Based on the analysis of advantages and disadvantages of the application of classic clustering algorithm in Web log data mining, the paper brought out a kind of hierarchical K-means Web log clustering algorithm, which integrated K-means clustering algorithm and cohesion-based hierarchical clustering algorithm and overcame shortcoming of high time complexity of hierarchical clustering algorithm. The clustering effect of the algorithm is better than K-means clustering and fit for clustering process of large amount data. The result analysis of practical Web log data clustering also proves the validity of the algorithm.

Info:

Periodical:

Key Engineering Materials (Volumes 439-440)

Edited by:

Yanwen Wu

Pages:

481-485

DOI:

10.4028/www.scientific.net/KEM.439-440.481

Citation:

L. X. Liu and Y. Q. Zhuang, "A Kind of Hierarchical K-Means Web Log Clustering Algorithm ", Key Engineering Materials, Vols. 439-440, pp. 481-485, 2010

Online since:

June 2010

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Price:

$35.00

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