A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering

Ratanamahatan, Chotirat, Keogh, Eamonn, Bagnall, Anthony J. and Lonardi, Stefano (2005) A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. In: Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, 3518 . Springer Berlin / Heidelberg, pp. 51-65. ISBN 978-3-540-26076-9

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Abstract

Because time series are a ubiquitous and increasingly prevalent type of data, there has been much research effort devoted to time series data mining recently. As with all data mining problems, the key to effective and scalable algorithms is choosing the right representation of the data. Many high level representations of time series have been proposed for data mining. In this work, we introduce a new technique based on a bit level approximation of the data. The representation has several important advantages over existing techniques. One unique advantage is that it allows raw data to be directly compared to the reduced representation, while still guaranteeing lower bounds to Euclidean distance. This fact can be exploited to produce faster exact algorithms for similarly search. In addition, we demonstrate that our new representation allows time series clustering to scale to much larger datasets.

Item Type:	Book Section
Additional Information:	Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-05) Hanoi, Vietnam, May 18-20, 2005
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Vishal Gautam
Date Deposited:	14 Jun 2011 11:52
Last Modified:	29 Jan 2025 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22609
DOI:

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