HMM-based hybrid meta-clustering ensemble for temporal data

Temporal data have many distinct characteristics, including high dimensionality, complex time dependency, and large volume, all of which make the temporal data clustering more challenging than conventional static datasets. In this paper, we propose a HMM-based partitioning ensemble based on hierarchical clustering refinement to solve the problems of initialization and model selection for temporal data clustering. Our approach results four major benefits, which can be highlighted as: (i) the model initialization problem is solved by associating the ensemble technique; (ii) the appropriate cluster number can be automatically determined by applying proposed consensus function on the multiple partitions obtained from the target dataset during clustering ensemble phase; (iii) no parameter re-estimation is required for the new merged pair of cluster, which significantly reduces the computing cost of its final refinement process based on HMM agglomerative clustering; and finally (iv) the composite model is better in characterizing the complex structure of clusters. Our approach has been evaluated on synthetic data and time series benchmark, and yields promising results for clustering tasks.