Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939448 | Pattern Recognition | 2018 | 13 Pages |
Abstract
To improve the performance of ensemble techniques for temporal data clustering, we propose a novel bi-weighted ensemble in this paper to solve the initialization and automated model selection problems encountered by all HMM-based clustering techniques and their applications. Our proposed ensemble features in a bi-weighting scheme in the process of examining each partition and optimizing consensus function on these input partitions in accordance with their level of importance. Within our proposed scheme, the multiple partitions, generated by HMM-based K-models under different initializations, are optimally re-consolidated into a representation of bi-weighted hypergraph, and the final consensus partition is generated and optimized via the agglomerative clustering algorithm in association with a dendrogram-based similarity partitioning (DSPA). In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Yun Yang, Jianmin Jiang,