In this paper a piecewise-dependent-data (PDD) clustering algorithm is presented, and a proof of its convergence to a local minimum is given. A distortion measure-based model represents each cluster. The proposed algorithm is iterative. At the end of each iteration, a competition between the models is performed. Then the data is regrouped between the models. The “movement” of the data between the models and the retraining allows the minimization of the overall system distortion. The Kohonen Self-Organizing Map (SOM) was used as the VQ model for clustering. The clustering algorithm was tested using data generated from four generators of Continuous Density HMM (CDHMM). It was demonstrated that the overall distortion is a decreasing function.
|Title of host publication||WSOM|
|Number of pages||7|
|State||Published - 2001|