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RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
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SERA '05 Proceedings of the Third ACIS Int'l Conference on Software Engineering Research, Management and Applications
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Towards mobility-based clustering
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Robust ensemble learning for mining noisy data streams
Decision Support Systems
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Precise anytime clustering of noisy sensor data with logarithmic complexity
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Proceedings of the 20th ACM international conference on Information and knowledge management
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Pattern Recognition Letters
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Existing data-stream clustering algorithms such as CluStream arebased on k-means. These clustering algorithms are incompetent tofind clusters of arbitrary shapes and cannot handle outliers. Further, they require the knowledge of k and user-specified time window. To address these issues, this paper proposes D-Stream, a framework for clustering stream data using adensity-based approach. The algorithm uses an online component which maps each input data record into a grid and an offline component which computes the grid density and clusters the grids based on the density. The algorithm adopts a density decaying technique to capture the dynamic changes of a data stream. Exploiting the intricate relationships between the decay factor, data density and cluster structure, our algorithm can efficiently and effectively generate and adjust the clusters in real time. Further, a theoretically sound technique is developed to detect and remove sporadic grids mapped to by outliers in order to dramatically improve the space and time efficiency of the system. The technique makes high-speed data stream clustering feasible without degrading the clustering quality. The experimental results show that our algorithm has superior quality and efficiency, can find clusters of arbitrary shapes, and can accurately recognize the evolving behaviors of real-time data streams.