Research Article
Improved K-Means Algorithm and Its Application to Vehicle Steering Identification
@INPROCEEDINGS{10.1007/978-3-319-73317-3_44, author={Hui Qi and Xiaoqiang Di and Jinqing Li and Hongxin Ma}, title={Improved K-Means Algorithm and Its Application to Vehicle Steering Identification}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={K-means Clustering Vehicle steering Vehicle navigation system}, doi={10.1007/978-3-319-73317-3_44} }- Hui Qi
Xiaoqiang Di
Jinqing Li
Hongxin Ma
Year: 2018
Improved K-Means Algorithm and Its Application to Vehicle Steering Identification
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_44
Abstract
K-means is a very common clustering algorithm, whose performance depends largely on the initially selected cluster center. The K-means algorithm proposed by this paper uses a new strategy to select the initial cluster center. It works by calculating the minimum and maximum distances from data to the origin, dividing this range into several equal ranges, and then adjusting every range according to the data distribution to equate the number of data contained in the ranges as much as possible, and finally calculating the average of data in every range and taking it as initial cluster center. The theoretical analysis shows that despite linear time complexity of initialization process, this algorithm has the features of an superlinear initialization method. The application of this algorithm to the analysis of GPS data when vehicle is moving shows that it can effectively increase the clustering speed and finally achieve better vehicle steering identification.