2009 SelfAdaptiveAnytimeStreamC

• (Kranen et al., 2009) ⇒ Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl. (2009). “Self-Adaptive Anytime Stream Clustering.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.47

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Abstract

• Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current onstraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 SelfAdaptiveAnytimeStreamC	Philipp Kranen Ira Assent Corinna Baldauf Thomas Seidl			Self-Adaptive Anytime Stream Clustering		ICDM 2009 Proceedings		10.1109/ICDM.2009.47		2009