关键词: 日志数据, 数据挖掘, 隐马尔科夫模型, 聚类

Abstract: Based on the more and more frequent visiting to the official website of the colleges or universities by candidates for colleges and their parents, it is very useful for the improvement of the website to understand the internet users’ accessing purpose and browsing behaviors. This paper combines the hidden Markov model and hierarchical clustering to perform the data mining of dynamic web log data. The original data are transformed by extension of the hidden Markov model and Symmetric Kullback-Leibler （SKL） distance into probabilistic space. Using hierarchical clustering on the SKL confusion matrix, the time series data can be clustered. This method is verified with a dynamic log data of Internet users’ accessing and browsing behaviors lasting for 2 months when the candidates for college and their parents are looking for a proper university to enter. The result shows that there are two patterns of users’ behaviors. This indicates that the method has a very good performance in feasibility and effectiveness.

Key words: log data, data mining, hidden Markov model, clustering