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Social network user position feature extraction method and device based on Mean shift and K-means clustering

A feature extraction, social network technology, applied in data processing applications, character and pattern recognition, special data processing applications, etc., can solve the problem of inability to adapt to the uneven data density of LBSNs, and achieve the effect of low time and space complexity

Active Publication Date: 2021-01-29
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that a single algorithm cannot adapt to the large amount of LBSNs data and uneven density, and proposes a social network user location feature extraction method based on Meanshift and K-means integrated clustering algorithm to achieve more effective LBSNs user location feature extraction

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  • Social network user position feature extraction method and device based on Mean shift and K-means clustering
  • Social network user position feature extraction method and device based on Mean shift and K-means clustering
  • Social network user position feature extraction method and device based on Mean shift and K-means clustering

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specific Embodiment

[0058] In a specific embodiment, the method comprises the steps of:

[0059] Step 1: analyzing and preprocessing the pre-collected user check-in data; preferably, collecting user check-in data from the Flickr platform;

[0060] (1-1) With the help of ArcGIS, describe the distribution of check-in records in the data set by drawing a scatter diagram, and select Manhattan, New York, where the check-in records are very dense, as the object area of ​​the present invention. Let the geographical location information data set of user check-in be L, which can be expressed as L=(p 1 ,p 2 ,...,p m ), where p i =(lat i , lon i ) represents the geographic location coordinates of the i-th check-in data—latitude and longitude;

[0061] (1-2) Data cleaning, removing data with missing fields and obviously wrong data in the data.

[0062] Step 2: Preliminary clustering of the check-in data within the city based on the Meanshift method;

[0063] (2-1) Record any two check-in points r i ...

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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 invention, the position feature extraction of the LBSNs data can be more effectively realized.

Description

technical field [0001] The invention belongs to the field of intelligent information processing and data mining, in particular to the application and mining of massive user sign-in data in location-based social networks (Location-based social networks, LBSNs), and in particular to an integrated aggregation method based on Meanshift and K-means A method and device for extracting location features of social network users based on an algorithm. Background technique [0002] Advances in Mobile Internet (Mobile Internet) and Global Positioning System (GPS) technologies have led to the rapid development of Location-based social networks (LBSNs), thus accumulating massive amounts of check-in data. The rapid development of LBSNs provides a wealth of information, which greatly enriches the availability of human mobile data and brings many aspects of value. On the one hand, compared with traditional social network data, LBSNs data includes social relationship data and comment data. I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9537G06F16/9536G06F16/29G06K9/62G06Q50/00
CPCG06F16/9537G06F16/9536G06F16/29G06Q50/01G06F18/23213
Inventor 史英吉王海艳吕朝萍何旭
Owner NANJING UNIV OF POSTS & TELECOMM