An ensemble of competitive learning networks with different representations for temporal data clustering

Temporal data clustering provides useful techniques for condensing and summarizing information conveyed in temporal data, which is demanded in various fields ranging from time series analysis to sequential data understanding. In this paper, we propose a novel approach to temporal data clustering by an ensemble of competitive learning networks incorporated by different representations of temporal data. In our approach, competitive learning networks of the rival-penalized learning mechanism are employed for clustering analyses based on different temporal data representations 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 different representations. Thanks to its capability of the rival penalized learning rules in automatic model selection and the synergy of fusing diverse partitions on different representations, our ensemble approach yields favorite results, which has been demonstrated in time series and motion trajectory clustering tasks.