Hidden Markov model

Weighting

Data mining

Computer science

Initialization

Radiology

Pattern recognition (psychology)

Mathematics

Hierarchical clustering

Artificial intelligence

Partition (number theory)

CURE data clustering algorithm

Cluster analysis

Combinatorics

Medicine

Programming language

Correlation clustering

Consensus clustering

Machine learning

IEEE Transactions on Cybernetics

Temporal data clustering can provide underpinning techniques for the discovery of intrinsic structures, which proved important in condensing or summarizing information demanded in various fields of information sciences, ranging from time series analysis to sequential data understanding. In this paper, we propose a novel hidden Markov model (HMM)-based hybrid meta-clustering ensemble with bi-weighting scheme to solve the problems of initialization and model selection associated with temporal data clustering. To improve the performance of the ensemble techniques, the proposed bi-weighting scheme adaptively examines the partition process and hence optimizes the fusion of consensus functions. Specifically, three consensus functions are used to combine the input partitions, generated by HMM-based K -models under different initializations, into a robust consensus partition. An optimal consensus partition is then selected from the three candidates by a normalized mutual information-based objective function. Finally, the optimal consensus partition is further refined by the HMM-based agglomerative clustering algorithm in association with dendrogram-based similarity partitioning algorithm, leading to the advantage that the number of clusters can be automatically and adaptively determined. Extensive experiments on synthetic data, time series, and real-world motion trajectory datasets illustrate that our proposed approach outperforms all the selected benchmarks and hence providing promising potentials for developing improved clustering tools for information analysis and management.

Adaptive Bi-Weighting Toward Automatic Initialization and Model Selection for HMM-Based Hybrid Meta-Clustering Ensembles