Time Series Clustering Based on ICA for Stock Data Analysis

Time series clustering is an important task in time series data mining. Compared to traditional clustering problems, time series clustering poses additional difficulties. The unique structure of time series makes many traditional clustering methods unable to apply directly. This paper presents a nov...

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Bibliographic Details
Published in2008 4th International Conference on Wireless Communications, Networking and Mobile Computing pp. 1 - 4
Main Authors Chonghui Guo, Hongfeng Jia, Na Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Clustering algorithms
Clustering methods
Data analysis
Data engineering
Data mining
Independent component analysis
Partitioning algorithms
Predictive models
Principal component analysis
Signal processing algorithms
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Summary:Time series clustering is an important task in time series data mining. Compared to traditional clustering problems, time series clustering poses additional difficulties. The unique structure of time series makes many traditional clustering methods unable to apply directly. This paper presents a novel feature-based approach to time series clustering, which first converts the raw time series data into feature vectors of lower dimension by using ICA algorithm, and then applies a modified k-means algorithm to the extracted feature vectors. Finally, to validate effectiveness and feasibility of the presented method, we use it to analyze the real world stock time series data and achieve reasonable results.
ISBN:9781424421077
1424421071
ISSN:2161-9646
DOI:10.1109/WiCom.2008.2534