Social network user position feature extraction method and device based on Mean shift and K-means clustering
The invention discloses a social network user position feature extraction method and device based on a Meanshift and K-means algorithm, and the method is used for solving a problem that a higher hot spot region, i.e., a position where a user is truly interested, in user sign-in frequency is found in...
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Format | Patent |
Language | Chinese English |
Published |
29.01.2021
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Abstract | The invention discloses a social network user position feature extraction method and device based on a Meanshift and K-means algorithm, and the method is used for solving a problem that a higher hot spot region, i.e., a position where a user is truly interested, in user sign-in frequency is found in massive user sign-in data. The implementation process of the method comprises the following steps:firstly, analyzing and preprocessing user sign-in data collected from a Flickr platform, selecting an area with dense and typical sign-in points as a research area, and then carrying out preliminary clustering on the sign-in data in a certain city range based on a Meanshift method; and carrying out secondary clustering on the screened large-scale clusters and excessively dense clusters based on aK-means method, and finally, according to a clustering result, carrying out division to corresponding POIs (Point of Interest), thereby completing user position feature extraction. By adopting the method provided by the invent |
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AbstractList | The invention discloses a social network user position feature extraction method and device based on a Meanshift and K-means algorithm, and the method is used for solving a problem that a higher hot spot region, i.e., a position where a user is truly interested, in user sign-in frequency is found in massive user sign-in data. The implementation process of the method comprises the following steps:firstly, analyzing and preprocessing user sign-in data collected from a Flickr platform, selecting an area with dense and typical sign-in points as a research area, and then carrying out preliminary clustering on the sign-in data in a certain city range based on a Meanshift method; and carrying out secondary clustering on the screened large-scale clusters and excessively dense clusters based on aK-means method, and finally, according to a clustering result, carrying out division to corresponding POIs (Point of Interest), thereby completing user position feature extraction. By adopting the method provided by the invent |
Author | SHI YINGJI WANG HAIYAN LYU CHAOPING HE XU |
Author_xml | – fullname: LYU CHAOPING – fullname: HE XU – fullname: WANG HAIYAN – fullname: SHI YINGJI |
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DocumentTitleAlternate | 基于Meanshift和K-means聚类的社交网络用户位置特征提取方法和装置 |
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Snippet | The invention discloses a social network user position feature extraction method and device based on a Meanshift and K-means algorithm, and the method is used... |
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SubjectTerms | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
Title | Social network user position feature extraction method and device based on Mean shift and K-means clustering |
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