Optimal K-Means Clustering Algorithm for Weblog Mining

Vipin Jain, K. Kashyap
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Abstract

World wide web (WWW) generates a huge number of unstructured data and information. The information is stored in weblog file. Weblogs information can be analyzed and visualized by various clustering algorithm. In this work, the k-means clustering algorithm is applied for grouping of the users with similar interest based on accessing of similer information. The optimal value of k is also determined by Elbow method to obtained optimal numbers of clusters. Clustering results are analyzed by various values of k. Comparative analysis ofvarious methods used for selecting the optimal number of clusters are also analyzed.
博客挖掘的最优k均值聚类算法
万维网(WWW)产生了大量的非结构化数据和信息。该信息存储在weblog文件中。Weblogs信息可以通过各种聚类算法进行分析和可视化。在这项工作中,基于对相似信息的访问,采用k-means聚类算法对具有相似兴趣的用户进行分组。通过肘部法确定k的最优值,得到最优簇数。通过不同的k值对聚类结果进行分析,并对选择最优聚类数的各种方法进行比较分析。
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