NOTE: The following materials are presented for timely dissemination of academic and technical work. Copyright and all other rights therein are reserved by authors and/or other copyright holders. Persoanl use of the following materials is permitted and, however, people using the materials or information are expected to adhere to the terms and constraints invoked by the related copyright.

Combining Competitive Learning Networks of Various Representations for Sequential Data Clustering


Sequential data clustering provides useful techniques for condensing and summarizing information conveyed in sequential data, which is demanded in various fields ranging from time series analysis to video clip understanding. In this chapter, we propose a novel approach to sequential data clustering by combining multiple competitive learning networks incorporated by various representations of sequential data and thus the clustering will be performed in the feature space. In our approach, competitive learning networks of a rival-penalized learning mechanism are employed for clustering analyses based on different sequential data representations individually while an optimal selection function is applied to find out a final consensus partition from multiple partition candidates yielded by applying alternative consensus functions to results of competitive learning on various representations. Thanks to its capability of the rival penalized learning rules in automatic model selection and the synergy of diverse partitions on various representations resulting from diversified initialization and stopping conditions, our ensemble learning approach yields favorite results especially in model selection, i.e. no assumption on the number of clusters underlying a given data set is needed prior to clustering analysis, which has been demonstrated in synthetic time series and motion trajectory clustering analysis tasks.

Click Chapter13.pdf for full text