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An Incremental Density Clustering Algorithm for Chaotic Time Series

Hui Li, Dechang Pi, Min Jiang


With the rapid development of information technology, large amounts of time series data are collected. So extract knowledge from these data has become an interesting issue. Most existing clustering algorithms divide time series data into a set of line segments, and measure them using point distance. They can discover time series clusters, but have poor robustness. Though the angle vector is used to measure the time sequences in the angle distance similarity measure (ADSM) algorithm, the measure is not accurate enough for lacking consideration of the line segments length. In this paper, drawing on the experience of time series to analyze the chaotic time series, a weight-based angle distance similarity measure algorithm (WBADSM) is proposed to make the similarity measure of time series more accurately. Based on this, an incremental density clustering algorithm (IDCWAD) is presented in this paper, which can reduce the computational resources and improve the efficiency of the clustering. The experimental results show that the proposed algorithms have good performance on effectiveness and efficiency.


chaotic time series, density-based, weighted angle, incremental clustering.

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