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论文类型：会议论文

发表时间：2008-10-12

收录刊物：EI、CPCI-S、Scopus

页面范围：10903-+

关键字：time series clustering; independent component analysis; stock data

摘要：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.