Segmentation of time series by the clustering and genetic algorithms

S. Tseng, Chun Hao Chen, Chien Hsiang Chen, Tzung Pei Hong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

This paper proposes a time series segmentation approach by combining the clustering technique, the discrete wavelet transformation and the genetic algorithm to automatically find segments and patterns from a time series. The genetic algorithm is used to find the segmentation points for deriving appropriate patterns. In the fitness evaluation, the proposed algorithm first divides subsequences in a chromosome into k clusters by using the k-means clustering approach. The Euclidean distance is then used to calculate the distance of each subsequence and evaluate a chromosome. The discrete wavelet transformation is also used to adjust the length of the subsequences for comparing their similarity since their length may be different. Experimental results show that the proposed approach can get good effects in finding appropriate segmentation patterns in time series.

Original languageEnglish
Title of host publicationProceedings - ICDM Workshops 2006 - 6th IEEE International Conference on Data Mining - Workshops
Pages443-447
Number of pages5
DOIs
StatePublished - 1 Dec 2006
Event6th IEEE International Conference on Data Mining - Workshops, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200618 Dec 2006

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference6th IEEE International Conference on Data Mining - Workshops, ICDM 2006
CountryChina
CityHong Kong
Period18/12/0618/12/06

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