An Efficient and Effective Clustering Algorithm for Time Series of Hot Topics
Han Zhong · Chinese Journal of Computers · 2012
Hot degree time series clustering is very important for revealing and modeling development process of hot topics in Web sites.In 2010,Leskovec and his colleagues proposed a K-Spectral Centroid(K_SC) time series clustering algorithm,which has higher accuracy and can be used to better describe the trend of hot topics.But K_SC algorithm is sensitive to the initialization of cluster centers and has high time complexity.Therefore,it is difficult to directly apply K_SC to high dimensional data.Based on wavelet transform technology,a new iteration clustering algorithm —WKSC is proposed in this paper,which has two improvements:(1) the original time series are compressed by Haar wavelets transform to lower dimensions of the original time series.WKSC algorithm groups topics based on lower dimensions time series and the time complexity is reduced;(2) the clustering results from previous iteration of K_SC are used as the initial assignment at the high level,then the high sensitivity to cluster centers is solved.Three datasets from different sources were selected and comprehensive experiments were conducted.Experimental results show that WKSC algorithm can significantly reduce time complexity,and improve the quality of clustering result,which means WKSC algorithm can be used on massive and high dimension hot topics.