scholarly journals Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering

2018 ◽  
Vol 231 ◽  
pp. 331-342 ◽  
Author(s):  
Kehua Li ◽  
Zhenjun Ma ◽  
Duane Robinson ◽  
Jun Ma
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hierarchical Clustering ◽  
Gaussian Mixture ◽  
Model Based Clustering ◽  
Model Based

scholarly journals Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering

2022 ◽  
Vol 32 (1) ◽  
pp. 361-375
Author(s):  
S. Markkandan ◽  
S. Sivasubramanian ◽  
Jaison Mulerikkal ◽  
Nazeer Shaik ◽  
Beulah Jackson ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Massive Mimo ◽  
Gaussian Mixture ◽  
Model Based Clustering ◽  
Codebook Design ◽  
Model Based

scholarly journals Gaussian Mixture Model Based Hierarchical Clustering in Prediction of Autism Spectrum Disorder

2019 ◽  
Vol 8 (3) ◽  
pp. 5751-5756
Keyword(s):  
Autism Spectrum Disorder ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hierarchical Clustering ◽  
Classification Accuracy ◽  
Gaussian Mixture ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Model Based ◽  
Proper Diagnosis

Autism is one of the most complex and divergent class disorders which accompany various lacking in symptoms needed for classification, societal interaction, abridged verbal communication, and monotonous behavior. Timely and proper diagnosis of Autism Spectrum Disorder can ensure the offering of medical treatment and guidance to get cure. In this paper, Gaussian Mixture Model based Hierarchical Clustering is proposed for efficiently predicting the Autism Spectrum Disorder. Also, Flexible splitting concept was proposed for hierarchical clustering in order to increase the quality of guessing and classification accuracy. The proposed algorithm is validated to check the performance against the existing method. The results shows that the proposed algorithm outperforms the existing algorithm in terms of classification accuracy.

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