Finding Time Series Discord Based on Bit Representation Clustering

Autor: Guiling Li, Olli Bräysy, Liangxiao Jiang, Zongda Wu, Yuanzhen Wang
Přispěvatelé: Information, Logistics and Innovation, Business Web and Media, Network Institute, Amsterdam Business Research Institute
Jazyk: angličtina
Rok vydání: 2013
Předmět:
Zdroj: Li, G, Braysy, O M P, Jiang, L, Wu, Z & Wang, Y 2013, ' Finding Time Series Discord Based on Bit Representation Clustering ', Knowledge-Based Systems, vol. 54, pp. 243-254 . https://doi.org/10.1016/j.knosys.2013.09.015
Knowledge-Based Systems, 54, 243-254. Elsevier
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2013.09.015
Popis: The problem of finding time series discord has attracted much attention recently due to its numerous applications and several algorithms have been suggested. However, most of them suffer from high computation cost and cannot satisfy the requirement of real applications. In this paper, we propose a novel discord discovery algorithm BitClusterDiscord which is based on bit representation clustering. Firstly, we use PAA (Piecewise Aggregate Approximation) bit serialization to segment time series, so as to capture the main variation characteristic of time series and avoid the influence of noise. Secondly, we present an improved K-Medoids clustering algorithm to merge several patterns with similar variation behaviors into a common cluster. Finally, based on bit representation clustering, we design two pruning strategies and propose an effective algorithm for time series discord discovery. Extensive experiments have demonstrated that the proposed approach can not only effectively find discord of time series, but also greatly improve the computational efficiency. © 2013 Elsevier B.V. All rights reserved.
Databáze: OpenAIRE
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