Research on Interactive Visualization Clustering Method Based on the Radar Chart

2012 ◽  
Vol 241-244 ◽  
pp. 1633-1639 ◽  
Author(s):  
Hui Jun Li ◽  
Zhi Quan Li ◽  
Jing Xuan Peng ◽  
Li Hui Zhang
Keyword(s):  
Data Clustering ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Not Given ◽  
Entropy Weight ◽  
Attribute Weights ◽  
Point Density ◽  
Radar Chart ◽  
Visualization Technology ◽  
Polar Radius

Most algorithms for the high-dimensional data clustering are not intuitive and the clustering results are difficult to explain. To solve these problems, a new method based on the interactive visualization technology was proposed in this paper. First, the entropy-weight was adopted to determine the main attributes and how to arrange them. Every data was described in an improved radar chart in which polar radius stood by attribute values and polar angles stood by the attribute weights. Then the points in the radar chart were clustered through applying an improved k-means algorithm. The number of clusters was not given before. And initial centers were optimized according to the point density and their distance. Finally, the experiment showed that the improved radar chart reflected the distribution of the data better and that the improved k-means algorithm was more efficient and accuracy.

scholarly journals Tracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings

2021 ◽  
Vol 33 (3) ◽  
pp. 629-642
Author(s):  
Sana Talmoudi ◽  
Tetsuya Kanada ◽  
Yasuhisa Hirata ◽  
◽  
Keyword(s):  
Real Time ◽  
Training Data ◽  
High Dimensional ◽  
Time Data ◽  
Early Failure ◽  
Rolling Bearings ◽  
Full Spectrum ◽  
Time Frequency ◽  
Vibration Data ◽  
Visualization Technology

Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possibility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

IQRAM: a high dimensional data clustering technique

2012 ◽  
Vol 2 (2/3) ◽  
pp. 117
Author(s):  
Dharmveer Singh Rajput ◽  
Pramod Kumar Singh ◽  
Mahua Bhattacharya
Keyword(s):  
Data Clustering ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Clustering Technique

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals A Method of Determining Multi-Attribute Weights Based on Single-Valued Neutrosophic Numbers and Its Application in TODIM

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 506 ◽  
Author(s):  
Dongsheng Xu ◽  
Yanran Hong ◽  
Kaili Xiang
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Entropy Weight ◽  
Attribute Weights ◽  
Attribute Weight ◽  
Decision Making Problem ◽  
Entropy Weight Method ◽  
Todim Method ◽  
Multi Attribute Decision Making ◽  
Weight Method

In this paper, the TODIM method is used to solve the multi-attribute decision-making problem with unknown attribute weight in venture capital, and the decision information is given in the form of single-valued neutrosophic numbers. In order to consider the objectivity and subjectivity of decision-making problems reasonably, the optimal weight is obtained by combining subjective weights and objective weights. Subjective weights are given directly by decision makers. Objective weights are obtained by establishing a weight optimization model with known decision information, then this method will compare with entropy weight method. These simulation results also validate the effectiveness and reasonableness of this proposed method.

Sign in / Sign up

Export Citation Format

Share Document