Detection of unique temporal segments by information theoretic meta-clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
L2GClust: local-to-global clustering of stream sources
Proceedings of the 2011 ACM Symposium on Applied Computing
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
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Event detection is a critical task in sensor networks, especially for environmental monitoring applications. Traditional solutions to event detection are based on analyzing one-shot data points, which might incur a high false alarm rate because sensor data is inherently unreliable and noisy. To address this issue, we proposea novel Distributed Single-pass Incremental Clustering (DSIC) technique to cluster the time series obtained at sensor nodes based on their underlying trends. In order to achieve scalability and energy-efficiency, our DSIC technique uses a hierarchical structure of sensor networks as the underlying infrastructure. The algorithm first compresses the time series produced at individual sensor nodes into a compact representation using Haar wavelettransform, and then, based on dynamic time warping distances, hierarchically groups the approximate time series into a global clustering model in an incremental manner. Experimental results on both real data and synthetic data demonstrate that our DSIC algorithm is accurate, energy-efficient and robust with respect tonetwork topology changes.