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A Clustering Algorithm for Time Series Data

Authors: Jian Yin 0001; Duanning Zhou; Qiong-Qiong Xie;

A Clustering Algorithm for Time Series Data

Abstract

In the Intelligent Traffic System, the research about the analysis of time series of traffic flow is important and meaningful. Using clustering methods to analyze time series not only can find some typical patterns of traffic flow, but also can group the sections of highway by their different flow characteristics. In this paper, we propose an Encoded-Bitmap-approach-based swap method to improve the classic hierarchical method. Experiments show that the proposed method has a better performance on the change trend of time series than classic algorithm.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Top 10%
Average
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