关键词: 标签聚类, 标签共现, K-means, 皮尔森系数, 特征向量

Abstract: In the social network, tag clustering analysis can deal with problems such as tag redundancy and semantic fuzziness and so on. In order to improve the effectiveness of clustering, it proposes to integrate label co-occurrence information and derive the feature vector of label, extracts the feature vector to calculate the similarity. The traditional clustering algorithm uses the geometric distance to calculate the distance to the object and the center of the object, now uses the Pearson correlation coefficient to calculate. The tag clustering algorithm that combines with K-means clustering algorithm to cluster label is proposed, and then analyzes the complexity of the algorithm. Finally, doing relevant comparative experiments for different clustering algorithms, the experimental results show that the proposed clustering algorithm enhances the clustering performance than other clustering algorithms, and verify the availability and effectiveness of the proposed clustering algorithm.

Key words: tag clustering, tag co-occurrence, K-means, Pearson?correlation coefficient, feature vector