This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis of evolving time series phenomena. It can maintain evolving clustering solutions separately for each stream/view and consensus clustering solutions reflecting evolving interrelations among the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. The properties of the MultiStream EvolveCluster algorithm are studied and evaluated with respect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation results show a stable algorithm performance in synthetic data scenarios. In the case of real-world data, the algorithm behavior demonstrates sensitivity to the individual streams’ data quality and the used consensus clustering technique.
Article ID: 2023L3
Publisher: Canadian Artificial Intelligence Association