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.
Extracting the User’s Interests from Web Log Data using A Time Based Algorithm