Sign-in data based user behavior trajectory clustering method

A trajectory clustering and user technology, applied in data processing applications, special data processing applications, geographic information databases, etc., can solve problems such as no users, data sparsity, and large differences in check-in times.

A trajectory clustering and user technology, applied in data processing applications, special data processing applications, geographic information databases, etc., can solve problems such as no users, data sparsity, and large differences in check-in times.

CN105243128AActive Publication Date: 2016-01-13中图科信数智技术(北京)有限公司
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  • Sign-in data based user behavior trajectory clustering method

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Embodiment Construction

[0023] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0024] as attached figure 1 Shown, the inventive method carries out according to the following steps:

[0025] Step 1: Obtain user check-in data, including user ID, check-in location, check-in time, and check-in date;

[0026] Location-based mobile social networks (LBSN) such as Sina Weibo, Jiepang, Renren, Foursquare, and Gowalla have developed rapidly in recent years. A large number of users use these services to record spatio-temporal behavior trajectories in the form of check-in. Therefore, they can provide API to capture the required user sign-in data.

[0027] Step 2: Preprocessing the user check-in data, including filtering useless data, converting types and unifying formats;

[0028] The analysis shows that the invalid users in the location data (that is, users who rarely check in after registration, such as users with less than 5 check-ins) and location P...

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Abstract

The present invention discloses a sign-in data based user behavior trajectory clustering method. The method comprises: steps 1, acquiring user sign-in data; step 2, preprocessing the user sign-in data; step 3, on the basis of comprehensively considering the influence of a marginal effect of a user sign-in date and difference of times of sign-in, calculating a sign-in value, in a sign-in position, of a user; step 4, initializing a cluster center, and performing clustering by using a cosine similarity method; step 5, recalculating the cluster center, and performing re-clustering by using the cosine similarity method; and step 6, repeating the step 5 until the requirement of preset clustering precision is satisfied.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a method for clustering user behavior tracks based on check-in data. Background technique [0002] With the rapid development of my country's national economy and the acceleration of urbanization, traffic congestion has become an overall problem that affects the sustainable development of cities. In order to solve traffic congestion, the state attaches great importance to urban road traffic infrastructure and traffic management, and has invested a lot of manpower, material resources, and financial resources. After years of construction, urban traffic infrastructure has made great achievements. However, with the rapid increase of car ownership, the construction of traffic infrastructure can no longer meet the needs of traffic development, and urban road congestion and traffic safety have become urgent problems to be solved. As an important part of intelligent transportation, the traffi...

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Application Information

Patent Timeline
13 Jan 2016
Publication
CN105243128A
IPC
G06F17/30; G06Q50/00
CPC
G06F16/29; G06Q50/01
Inventors
刘兴伟; 夏梅宸