Selection method of K-means initial clustering centers for taxi trajectory data

Abstract

The invention relates to a selection method of K-means initial clustering centers for taxi trajectory data. The method comprises a step of extracting the road network of city traffic from an electronic map, a step of carrying out preprocessing on collected taxi trajectory data and obtaining sample data suitable for analysis by screening, a step of matching the taxi trajectory data and the road network to obtain a distribution map with taxi data points in a preset analysis range, a step of using the spot detection method in the image recognition technology to identify the main intensive region of taxi trajectory data points as the initial clustering centers of K-means, and a step of outputting the initial clustering centers of K-means. According to the method, through using the spot detection method to determine the position and number of the initial clustering centers of K-means, the defects of fuzziness, subjectivity and initial center random selection of selecting a K value in a traditional K-means method are overcome, for mass car networking data, the clustering speed of the K-Means method is speeded up, the clustering of the taxi trajectory data is realized, and the method has a certain reference value and an actual economic benefit.

Description

technical field [0001] The invention relates to the fields of machine learning and image recognition, in particular to a K-means initial clustering center selection method for taxi trajectory data. Background technique [0002] With the rapid development of the Internet, data has penetrated into all walks of life, and the massive data generated every day has gradually become the dominant production factor in the social economy. Floating car trajectory data is the main form of big data in the field of urban Internet of vehicles and intelligent transportation. Its processing and analysis will generate inestimable value for citizens' daily travel, urban planning and solving traffic congestion bottlenecks. [0003] Cluster analysis is an important research method in the field of machine learning and data mining. Since the clustering method does not make any statistical assumptions on the data, it is called an unsupervised learning. Cluster analysis refers to the grouping of dat...

AI Technical Summary

Problems solved by technology

However, since the K-means method randomly selects the initial clustering center, it cannot guarantee a unique clustering result, and the final clustering result directly depends on the selection of the initial clustering center

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