[1]刘 凯,龚兰兰,凌兴宏,等.改进聚类算法在公交数据挖掘中的应用研究[J].计算机技术与发展,2020,30(06):207-210.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 040]
　LIU Kai,GONG Lan-lan,LING Xing-hong,et al.Research of Improved Clustering Algorithm Applied in Bus Data Mining[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):207-210.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 040]
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改进聚类算法在公交数据挖掘中的应用研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
207-210
栏目:
应用开发研究
出版日期:
2020-06-10

文章信息/Info

Title:
Research of Improved Clustering Algorithm Applied in Bus Data Mining
文章编号:
1673-629X(2020)06--0207-04
作者:
刘 凯1 ; 龚兰兰1 ; 凌兴宏1; 2 ; 周家骎1
1. 苏州大学文正学院,江苏 苏州 215104; 2. 苏州大学 计算机科学与技术学院,江苏 苏州 215104
Author(s):
LIU Kai1 ; GONG Lan-lan1 ; LING Xing-hong1; 2 ; ZHOU Jia-qin1
1. Wenzheng College of Soochow University,Suzhou 215104,China; 2. School of Computer Science and Technology,Soochow University,Suzhou 215104,China
关键词:
枢纽站确定; 遗传算法; K-均值算法; 凝聚层次聚类; 类簇数
Keywords:
hub sites determining; genetic algorithm; K-means algorithm; hierarchical clustering algorithm; cluster number
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 040
摘要:
针对城市交通规划中枢纽站点确定的问题,提出了使用改进的聚类算法对公交数据进行处理来确定枢纽站点,同时在研究聚类算法的应用过程中,提出了使用遗传算法改进组合的聚类算法确定聚类算法中各层类簇数。 针对本实验公交枢纽站点确定的问题,为达到更优的聚类效果,将原始遗传算法与 K-means 算法及层次聚类算法三者结合。 聚类算法参数设置的方法,有别于传统确定类簇数的方法,使用遗传算法确定 K-means 算法与层次聚类算法结合时的类簇数,以及两个类簇数之间的关系。 通过基于真实数据的模拟实验表明,使用聚类算法确定枢纽站点为公交枢纽站点二次规划提供了可靠的数据支持,并且相对于传统的聚类算法,使用遗传算法改进的聚类算法的聚类效果有了较好的提升。
Abstract:
Aiming at the problem of determining hub sites in urban traffic planning, we propose an improved clustering algorithm to process the bus data to determine the hub sites. At the same time,in the process of studying the application of clustering algorithm,we propose an combination clustering algorithm improved by the genetic algorithm to determine the number of clusters of each layer in the clustering algorithm. In order to achieve better clustering effect,the original genetic algorithm is combined with K-means algorithm and hierarchical clustering algorithm. The method of clustering algorithm parameter setting is different from the traditional method of determining the cluster number. The genetic algorithm is used to determine the number of clusters and the relationship between the two cluster numbers when the K-means algorithm is combined with the hierarchical clustering algorithm. The simulation experiments basedon real data show that the clustering algorithm to determine the hub sites provides reliable data support for the secondary planning of the bus hub sites,and the clustering algorithm improved by the genetic algorithm has better clustering effect compared with the traditional clustering algorithm.

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更新日期/Last Update: 2020-06-10