TY - JOUR
T1 - Finding Time Series Discord Based on Bit Representation Clustering
AU - Li, G.
AU - Braysy, O.M.P.
AU - Jiang, L.
AU - Wu, Z.
AU - Wang, Y.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
U2 - 10.1016/j.knosys.2013.09.015
DO - 10.1016/j.knosys.2013.09.015
M3 - Article
SN - 0950-7051
VL - 54
SP - 243
EP - 254
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
ER -