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

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 segme...

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Vydáno v:2017 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation (SmartWorld SCALCOM UIC ATC CBDCom IOP SCI) s. 1 - 6
Hlavní autoři: Ma, Jun, Luo, Shen, Wu, Liuyang, Luo, Guofu, Li, Xiaoke, Zhang, Dong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2017
Témata:
Canopy Algorithm
Clustering algorithms
Computational modeling
Distributed databases
Highway Passenger Segmentation
Indexes
K-Means Algorithm
Parallel Computing
Parallel processing
Road transportation
Sparks
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Shrnutí: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.
DOI:10.1109/UIC-ATC.2017.8397634