A Comparative Study on k-means Clustering Method and Analysis

2019 ◽  
pp. 113-127
Author(s):  
Rajdeep Baruri ◽  
Anannya Ghosh ◽  
Saikat Chanda ◽  
Ranjan Banerjee ◽  
Anindya Das ◽  
...  
Keyword(s):  
Comparative Study ◽  
Clustering Method

scholarly journals Comparative Study on Thresholding

2011 ◽  
pp. 73-77
Author(s):  
K.C. Singh ◽  
Lalit Mohan Satapathy ◽  
Bibhudatta Dash ◽  
S.K. Routray
Keyword(s):  
Computational Complexity ◽  
Comparative Study ◽  
Hierarchical Cluster ◽  
Multilevel Thresholding ◽  
Clustering Method ◽  
Good Separation ◽  
Robust Estimate ◽  
Clear Separation ◽  
Cluster Organization ◽  
Multi Level

Criterion based thresholding algorithms are simple and effective for two-level thresholding. However, if a multilevel thresholding is needed, the computational complexity will exponentially increase and the performance may become unreliable. In this approach, a novel and more effective method is used for multilevel thresholding by taking hierarchical cluster organization into account. Developing a dendogram of gray levels in the histogram of an image, based on the similarity measure which involves the inter-class variance of the clusters to be merged and the intra-class variance of the new merged cluster . The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multilevel thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected numbers of clusters. This paper gives a comparison on Otsu’s & Kwon’s criterion with hierarchical based multi-level thresholding.

scholarly journals Comparative Study and Analysis of Students results using Clustering Techniques

2021 ◽  
Vol 9 (2) ◽  
pp. 835-842
Author(s):  
Mrs. Bhawna Janghel, Et. al.
Keyword(s):  
Academic Performance ◽  
Comparative Study ◽  
Data Clustering ◽  
Quality Education ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Clustering Technique ◽  
Weak Student ◽  
Using Data

In this paper using clustering method for student’s school academic performance are measured from same district. By using data clustering technique we can predict which school is best. And try to identify the weak student of particular school and will identify the result of best school. This will show which school is better for observing the techniques in disrict.The best school will be help us to making the quality education.  

Author response for "Laminar distribution of cortical projection neurons to the pulvinar: A comparative study in cats and mice"

2020 ◽  
Author(s):  
Bruno Oliveira Ferreira de Souza ◽  
Éve‐Marie Frigon ◽  
Robert Tremblay‐Laliberté ◽  
Christian Casanova ◽  
Denis Boire
Keyword(s):  
Comparative Study ◽  
Author Response ◽  
Projection Neurons ◽  
Cortical Projection ◽  
Laminar Distribution
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