Generalized k means based clustering for temporal data under weighted and kernel time warp
Generalize k means based clustering to temporal data under time warp.Extend time warp measures and temporal kernels to capture local temporal differences.Propose a tractable estimation of the cluster representatives under extended measures.Propose fast solutions that capture both global and local temporal features.Deep analysis on a wide range of 20 non isotropic, linearly non separable public data. Temporal data naturally arise in various emerging applications, such as sensor networks, human mobility or internet of things. Clustering is an important task, usually applied a priori to pattern analysis tasks, for summarization, group and prototype extraction; it is all the more crucial for dimensionality reduction in a big data context. Clustering temporal data under time warp measures is challenging because it requires aligning multiple temporal data simultaneously. To circumvent this problem, costly k medoids and kernel k means algorithms are generally used. This work investigates a different approach to temporal data clustering through weighted and kernel time warp measures and a tractable and fast estimation of the representative of the clusters that captures both global and local temporal features. A wide range of 20 public and challenging datasets, encompassing images, traces and ecg data that are non isotropic (i.e. non spherical) not well isolated and linearly non separable, is used to evaluate the efficiency of the proposed temporal data clustering. The results of this comparison illustrate the benefits of the method proposed, which outperforms the baselines on all datasets. A deep analysis is conducted to study the impact of the data specifications on the effectiveness of the studied clustering methods.
Pattern Recognit. Lett.