Using Bisect K-Means Clustering Technique in the Analysis of Arabic Documents

In this article, I have investigated the performance of the bisect K-means clustering algorithm compared to the standard K-means algorithm in the analysis of Arabic documents. The experiments included five commonly used similarity and distance functions (Pearson correlation coefficient, cosine, Jaccard coefficient, Euclidean distance, and averaged Kullback-Leibler divergence) and three leading stemmers. Using the purity measure, the bisect K-means clearly outperformed the standard K-means in all settings with varying margins. For the bisect K-means, the best purity reached 0.927 when using the Pearson correlation coefficient function, while for the standard K-means, the best purity reached 0.884 when using the Jaccard coefficient function. Removing stop words significantly improved the results of the bisect K-means but produced minor improvements in the results of the standard K-means. Stemming provided additional minor improvement in all settings except the combination of the averaged Kullback-Leibler divergence function and the root-based stemmer, where the purity was deteriorated by more than 10%. These experiments were conducted using a dataset with nine categories, each of which contains 300 documents.