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Research on highway passenger segmentation based on Canopy-kmeans clustering algorithm under parallel computing framework | IEEE Conference Publication | IEEE Xplore

Research on highway passenger segmentation based on Canopy-kmeans clustering algorithm under parallel computing framework


Abstract:

In order to satisfy the needs of highway passenger precise segmentation with massive historical data, a novel clustering algorithm under parallel computing framework is p...Show More

Abstract:

In order to satisfy the needs of highway passenger precise segmentation with massive historical data, a novel clustering algorithm under parallel computing framework is proposed. The average number of tickets purchased in a period is considered to build an evaluation model of highway passenger segmentation CFMY. To accurately determine the initial center point and K value, Canopy algorithm is introduced to improve the K-means clustering algorithm. The improved k-means algorithm is conducted using the parallel computing framework in Spark platform. Finally, the proposed method using the parallel computing framework is applied to the highway passenger segmentation cluster analysis, where the CFMY model is used as the evaluation index. The effectiveness of the proposed method is verified by experiments.
Date of Conference: 04-08 August 2017
Date Added to IEEE Xplore: 28 June 2018
ISBN Information:
Conference Location: San Francisco, CA, USA

References

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