Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Information retrieval
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A two-round variant of EM for Gaussian mixtures
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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This paper presents a novel algorithm for document clustering based on a combinatorial framework of the Principal Direction Divisive Partitioning (PDDP) algorithm [1] and a simplified version of the EM algorithm called the spherical Gaussian EM (sGEM) algorithm. The idea of the PDDP algorithm is to recursively split data samples into two sub-clusters using the hyperplane normal to the principal direction derived from the covariance matrix. However, the PDDP algorithm can yield poor results, especially when clusters are not well-separated from one another. To improve the quality of the clustering results, we deal with this problem by re-allocating new cluster membership using the sGEM algorithm with different settings. Furthermore, based on the theoretical background of the sGEM algorithm, we can naturally extend the framework to cover the problem of estimating the number of clusters using the Bayesian Information Criterion. Experimental results on two different corpora are given to show the effectiveness of our algorithm.