Online Clustering of Multivariate Time-series
Masud Moshtaghi, Christopher A. Leckie, James C. Bezdek · 2016
The intrinsic nature of streaming data requires algorithms that are capable of fast data analysis to extract knowledge. Most current unsupervised data analysis techniques rely on the implementation of known batch techniques over a sliding window, which can hinder their utility for the analysis of evolving structure in applications involving large streams of data. This research presents a novel data clustering algorithm, which exploits the correlation between data points in time to cluster the data, while maintaining a set of decision boundaries to identify noisy or anomalous data. We illustrate the proposed algorithm for online clustering with numerical results on both real-life and simulated datasets, which demonstrate the efficiency and accuracy of our approach compared to existing methods.