scholarly journals Evaluating the Performance of 16 Egyptian Wheat Varieties Using Self-Organizing Map (SOM) and Cluster Analysis

2016 ◽  
Vol 16 (2) ◽  
pp. 47-53 ◽  
Author(s):  
O.M. Ibrahim ◽  
M.M. Tawfik Elh ◽  
A. Badr ◽  
Asal M. Wali
Keyword(s):  
Cluster Analysis ◽  
Self Organizing Map ◽  
Wheat Varieties ◽  
And Cluster Analysis ◽  
Self Organizing

scholarly journals Expanding Self-Organizing Map for data visualization and cluster analysis

2004 ◽  
Vol 163 (1-3) ◽  
pp. 157-173 ◽  
Author(s):  
Huidong Jin ◽  
Wing-Ho Shum ◽  
Kwong-Sak Leung ◽  
Man-Leung Wong
Keyword(s):  
Cluster Analysis ◽  
Data Visualization ◽  
Self Organizing Map ◽  
And Cluster Analysis ◽  
Self Organizing

scholarly journals Spatial distribution and cluster analysis of dengue using self organizing maps in Andhra Pradesh, India, 2011–2013

2018 ◽  
Vol 3 (1) ◽  
pp. 52-61 ◽  
Author(s):  
Srinivasa Rao Mutheneni ◽  
Rajasekhar Mopuri ◽  
Suchithra Naish ◽  
Deepak Gunti ◽  
Suryanarayana Murty Upadhyayula
Keyword(s):  
Cluster Analysis ◽  
Spatial Distribution ◽  
Andhra Pradesh ◽  
Self Organizing Maps ◽  
And Cluster Analysis ◽  
Self Organizing

Analysis of Significant Prognostic Factors of Patients with Bladder Cancer Using Self-Organizing Maps

2013 ◽  
Vol 199 ◽  
pp. 223-228
Author(s):  
Marta Kolasa ◽  
Rafał Długosz ◽  
Wojciech Jóźwicki ◽  
Jolanta Pauk ◽  
Aleksandra Świetlicka ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Bladder Cancer ◽  
Prognostic Factors ◽  
Histological Grade ◽  
Medical Data ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
New Approach ◽  
Highly Correlated ◽  
Self Organizing

This study presents a new approach to determine significant prognostic factors for patients suffering from the bladder cancer. The analysis of medical data has been performed by the use of the Kohonen self-organizing map (SOM). The SOM allows visualizing and identifying the prognostic factors indicating which of them are significant. A database comprised of ninety patients has been used in this study. Seven predictors were investigated. The cluster analysis indicates that the significant prognostic factors for the bladder cancer are: histological grade (cG) and stage (cT). The obtained results also showed that the sex and the cG variables are highly correlated and that the number of non-classic differentiation (NDNc) features in bladder cancer is somewhat correlated to surgically removed lymphnode number (LN) and metastatic positive lymphnode number (PLN).

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