Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2006.97
Title: Mining dense periodic patterns in time series data
Authors: Sheng, C.
Mong, W.H. 
Lee, L. 
Issue Date: 2006
Source: Sheng, C.,Mong, W.H.,Lee, L. (2006). Mining dense periodic patterns in time series data. Proceedings - International Conference on Data Engineering 2006 : 115-. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2006.97
Abstract: Existing techniques to mine periodic patterns in time series data are focused on discovering full-cycle periodic patterns from an entire time series. However, many useful partial periodic patterns are hidden in long and complex time series data. In this paper, we aim to discover the partial periodicity in local segments of the time series data. We introduce the notion of character density to partition the time series into variable-length fragments and to determine the lower bound of each character's period. We propose a novel algorithm, called DPMiner, to find the dense periodic patterns in time series data. Experimental results on both synthetic and real-life datasets demonstrate that the proposed algorithm is effective and efficient to reveal interesting dense periodic patterns. © 2006 IEEE.
Source Title: Proceedings - International Conference on Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/40915
ISBN: 0769525709
ISSN: 10844627
DOI: 10.1109/ICDE.2006.97
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

27
checked on Jan 22, 2018

Page view(s)

42
checked on Jan 20, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.