The Behavior Analysis of Stock Analysts Based on K-Prototypes Clustering Algorithm

2018 ◽  
Vol 08 (06) ◽  
pp. 894-901
Author(s):  
晓妹 张
Keyword(s):  
Behavior Analysis ◽  
Clustering Algorithm ◽  
Stock Analysts

Application of clustering algorithm in social network user scenario prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 4971-4979
Author(s):  
Xiaoxian Wen ◽  
Yunhui Ma ◽  
Jiaxin Fu ◽  
Jing Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Characteristic Analysis ◽  
User Behavior Analysis ◽  
Multimedia Social Networks ◽  
Scenario Prediction ◽  
User Scenario

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

Review of Applied Behavior Analysis.

1988 ◽  
Vol 33 (3) ◽  
pp. 271-271
Author(s):  
Cyril M. Franks
Keyword(s):  
Behavior Analysis ◽  
Applied Behavior Analysis

Basic research in behavior analysis.

2018 ◽  
Vol 18 (2) ◽  
pp. 117-118 ◽  
Author(s):  
Anita Li ◽  
Amanda Mahoney ◽  
Alan Poling
Keyword(s):  
Behavior Analysis ◽  
Basic Research
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