4.7 Article

Soft Subspace Based Ensemble Clustering for Multivariate Time Series Data

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3146136

Keywords

Clustering algorithms; Clustering methods; Time series analysis; Principal component analysis; Partitioning algorithms; Linear programming; Weight measurement; Ensemble clustering; hard subspace clustering; multivariate time series (MTS); soft subspace clustering

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In this study, a variable-weighted K-medoids clustering algorithm is proposed to address the issues of correlations and redundancies between variables in MTS data. In addition, a new approach is introduced to handle the initialization sensitivity problem, along with an ensemble clustering framework based on density peaks to further enhance the clustering performance.
Recently, multivariate time series (MTS) clustering has gained lots of attention. However, state-of-the-art algorithms suffer from two major issues. First, few existing studies consider correlations and redundancies between variables of MTS data. Second, since different clusters usually exist in different intrinsic variables, how to efficiently enhance the performance by mining the intrinsic variables of a cluster is challenging work. To deal with these issues, we first propose a variable-weighted K-medoids clustering algorithm (VWKM) based on the importance of a variable for a cluster. In VWKM, the proposed variable weighting scheme could identify the important variables for a cluster, which can also provide knowledge and experience to related experts. Then, a Reverse nearest neighborhood-based density Peaks approach (RP) is proposed to handle the problem of initialization sensitivity of VWKM. Next, based on VWKM and the density peaks approach, an ensemble Clustering framework (SSEC) is advanced to further enhance the clustering performance. Experimental results on ten MTS datasets show that our method works well on MTS datasets and outperforms the state-of-the-art clustering ensemble approaches.

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