Algorithms for clustering data
Algorithms for clustering data
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A unified framework for model-based clustering
The Journal of Machine Learning Research
Similarity-based classification of sequences using hidden Markov models
Pattern Recognition
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In this paper we consider sequence clustering problems and propose an algorithm for the estimation of the number of clusters based on the X-means algorithm. The sequences are modeled using mixtures of Hidden Markov Models. By means of experiments with synthetic data we analyze the proposed algorithm. This algorithm proved to be both computationally efficient and capable of providing accurate estimates of the number of clusters. Some results of experiments with real-world web-log data are also given.