An Improved Semantic Smoothing Model for Model-Based Document Clustering

Recently, semantic smoothing is proposed as an efficient solution for the improvement of document cluster quality. However, the existing semantic smoothing model is not effective for partitional clustering to enhance the document clustering quality. In this paper, inspired by the TF*IDF schema and background elimination strategy, we first introduce an improved semantic smoothing model, which is suitable for both agglomerative and partitional clustering. Based on the improved semantic smoothing model, two model-document clustering algorithms, the partitional clustering algorithm and the agglomerative clustering algorithm, are also presented. The experimental results show our algorithms are more effective than the previous methods to improve the cluster quality.