Improved blog clustering through automated weighting of text blocks
Hongbo Li, Yunming Ye, Joshua Zhexue Huang · 2009
In this paper, a new clustering algorithm is proposed for blog data clustering. Considering the structure information of text blocks in blog data, we group the features of blog data into three groups and extend the k-means clustering algorithm to automatically calculate a weight for each feature group in the clustering process. We introduce a new objective function with group weight variables and present the Lagrangian method to derive the formula to calculate the group weights. This formula is added as a new step in the standard k-means iterative clustering process to automatically compute the group weights according to the distribution of features. This new process guarantees the convergency of the clustering process to a local optimal solution. The experimental results have shown that this new algorithm performed better than k-means without group feature weighting on different blog data sets.