A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Enhancing principal direction divisive clustering
Pattern Recognition
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The Principal Direction Divisive Partitioning (PDDP) algorithm is a fast and scalable clustering algorithm [3]. The basic idea is to recursively split the data set into sub-clusters based on principal direction vectors. However, the PDDP algorithm can yield poot results, especially when cluster structures are not well-separated from one another. Its stopping criterion is based on a heuristic that often tends to overestimate the number of clusters. In this paper, we propose simple and efficient solutions to the problems by refining results from the splitting process, and applying the Bayesian Information Criterion (BIC) to estimate the true number of clusters. This motivates a novel algorithm for unsupervised clustering, which its experimental results on different data sets are very encouraging.