ITEM-USER PREFERENCE MAPPING WITH MIXTURE MODELS - Data Visualization for Item Preference

2009 ◽  
Keyword(s):  
Mixture Models ◽  
Data Visualization ◽  
User Preference ◽  
Preference Mapping

Data visualization via latent variables and mixture models: a brief survey

2015 ◽  
Vol 19 (3) ◽  
pp. 807-819 ◽  
Author(s):  
Rodolphe Priam ◽  
Mohamed Nadif
Keyword(s):  
Mixture Models ◽  
Data Visualization ◽  
Latent Variables

An emerging role for data visualization

2008 ◽  
Author(s):  
Pamela Ebert Flattau
Keyword(s):  
Data Visualization

Post Hoc Investigation of Factor Mixture Models

2007 ◽  
Author(s):  
Danielle L. Cisler ◽  
Gitta H. Lubke
Keyword(s):  
Mixture Models ◽  
Post Hoc

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnoses

2012 ◽  
Author(s):  
James Ong ◽  
Emilio Remolina ◽  
David Breeden ◽  
Brett Stroozas ◽  
John Mohammed
Keyword(s):  
Data Visualization
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