Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62116
Title: A new symbolization and distance measure based anomaly mining approach for hydrological time series
Authors: Zhang, P
Xiao, Y
Zhu, Y
Feng, J
Wan, D
Li, W
Leung, H 
Keywords: Data mining
Distance measure
Hydrological time series
Pattern representation
Issue Date: 2016
Publisher: IGI Global
Source: International journal of web services research, 2016, v. 13, no. 3, p. 26-45 How to cite?
Journal: International journal of web services research 
Abstract: Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data existing in various fields. Their key problems are high fitting error after dimension reduction and low accuracy of mining results. This paper studies an approach of mining time series abnormal patterns in the hydrological field. The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series. They propose Feature Points Symbolic Aggregate Approximation (FP-SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping (SD-DTW). Finally, the distances generated are sorted. A set of dedicated experiments are performed to validate the authors' approach. The results show that their approach has lower fitting error and higher accuracy compared to other approaches.
URI: http://hdl.handle.net/10397/62116
ISSN: 1545-7362
DOI: 10.4018/IJWSR.2016070102
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

14
Last Week
4
Last month
Checked on Aug 21, 2017

Google ScholarTM

Check

Altmetric



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