Text Clustering Based on a Divide and Merge Strategy

Experimental results showed that the dynamically updated center number prevent the deterioration of clustering result when k deviates from the real class numbers and FC-DM also outperformed other benchmark algorithms.

Abstract

A text clustering algorithm is proposed to overcome the drawback of division based clustering method on sensitivity of estimated class number. Complex features including synonym and co-occurring words are extracted to make a feature space containing more semantic information. Then the divide and merge strategy helps the iteration converge to a reasonable cluster number. Experimental results showed that the dynamically updated center number prevent the deterioration of clustering result when k deviates from the real class numbers. When k is too small or large, the difference of clustering results between FC-DM and k-means is more obvious and FC-DM also outperformed other benchmark algorithms.