Abstract:

This paper proposed a new idea for popular microblogging classification, by analyzing the users who forwarded the popular microblogging to obtain the clustering result, and distinguishing the different kinds of popular microblogging depending on the aggregation state of user. The user clustering algorithm is called X-means algorithm which improved on the basis of K-means clustering algorithm, and improved further according to the characteristics of the microblogging user. Taking into account the difference of the user themselves and their attributes, this paper used a weighted approach based on the logarithmic function in the process of improving X-means algorithm ,which can ensure that the clustering results more scientific and accurate. Simultaneously , this paper achieved a weighted approach for the special nodes by the way of establishing a Key-Personnel- Database, then this paper achieved the dynamic updates of the database with the HITS algorithm. After completing the user clustering, the experiment put the important user information into the Key-Personnel- Database in different fields, by which can achieve the feedback mechanism between the clustering processes and the database. In addition, clustered the microblogging user with the X-means algorithm and the k-means algorithm as well as their improved algorithm, and ultimately proved the improved X-means algorithm has more advantages in the microblogging user clustering.

Key words: microblogging classification, user clustering, outline coefficient