Rival Penalized Competitive Learning for Model-Based Sequence Clustering

Abstract

In this paper, we propose a model-based, competitive learning procedure for the clustering of variable-length sequences. Hidden Markov models (HMMs) are used as representations for the cluster centers, and rival penalized competitive learning (RPCL), originally developed for domains with static, fixed-dimensional features, is extended. State merging operations are also incorporated to favor the discovery of smaller HMMs. Simulation results show that our extended version of RPCL can produce a more accurate cluster structure than k-means clustering.

DOI: 10.1109/ICPR.2000.906046

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@inproceedings{Law2000RivalPC, title={Rival Penalized Competitive Learning for Model-Based Sequence Clustering}, author={Martin H. C. Law and James T. Kwok}, booktitle={ICPR}, year={2000} }