A0468
Title: FCPCA: Fuzzy clustering of high-dimensional MTS based on common principal component analysis with robust extensions
Authors: Ziling Ma - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) [presenting]
Angel Lopez Oriona - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia)
Hernando Ombao - King Abdullah University of Science and Technology (KAUST) (Saudi Arabia)
Ying Sun - KAUST (Saudi Arabia)
Abstract: Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional approaches, such as crisp clustering, rely on the assumption that clusters are sufficiently separated with little overlap. However, real-world data often defy this assumption, exhibiting overlapping distributions or overlapping clouds of points and blurred boundaries between clusters. Fuzzy clustering offers a compelling alternative by allowing partial membership in multiple clusters, making it well-suited for these ambiguous scenarios. Despite its advantages, current fuzzy clustering methods primarily focus on univariate time series, and for multivariate cases, even datasets of moderate dimensionality become computationally prohibitive. A novel fuzzy clustering approach is introduced based on common principal component analysis to address the aforementioned shortcomings. The method has the advantage of efficiently handling high-dimensional multivariate time series by reducing dimensionality while preserving critical temporal features. Extensive numerical results show that our proposed clustering method outperforms several existing approaches in the literature. An interesting application involving brain signals from different drivers recorded from a simulated driving experiment illustrates the potential of the approach. Meanwhile, the robust extensions of FCPCA will also be presented.
