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

A technology of initial clustering center and trajectory data, applied in data processing applications, instruments, character and pattern recognition, etc., can solve problems such as the inability to guarantee clustering results, and achieve the effect of facilitating decision analysis and improving clustering accuracy.

Inactive Publication Date: 2016-11-16
FUZHOU UNIVERSITY
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Problems solved by technology

However, since the K-means method randomly selects the initial clustering center, it cannot guarantee a unique clu

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

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[0031] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0032] The present invention proposes a kind of K-means initial clustering center selection method for taxi track data, specifically realizes according to the following steps:

[0033] Step S1, extracting main roads of urban traffic from the electronic map to form a road network.

[0034] In this embodiment, the electronic map is in vector format and includes various layers such as roads, airports, railway stations, shopping malls, tourist attractions, etc., forming a complete city function. The analysis of taxi GPS data mainly involves road layers, including urban expressways, national roads, provincial roads, county roads, township and village roads and other road layers.

[0035] The above-mentioned roads at all levels are extracted by using the MapInfo software of the geographic information system. Because each layer corresponds to ...

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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...

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Application Information

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IPC IPC(8): G06Q50/30G06K9/62
CPCG06Q50/30G06F18/23213
Inventor 冯心欣谢志鹏凌献尧徐艺文郑海峰陈忠辉
Owner FUZHOU UNIVERSITY
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