Robust Clustering

2016 ◽  
pp. 200-217
Keyword(s):  
Robust Clustering

Outlier-Robust Clustering of Gaussians and Other Non-Spherical Mixtures

2020 ◽  
Author(s):  
Ainesh Bakshi ◽  
Ilias Diakonikolas ◽  
Samuel B. Hopkins ◽  
Daniel Kane ◽  
Sushrut Karmalkar ◽  
...  
Keyword(s):  
Robust Clustering

scholarly journals Cluster analysis with cellwise trimming and applications for the robust clustering of curves

2021 ◽  
Author(s):  
L.A. García-Escudero ◽  
D. Rivera-García ◽  
A. Mayo-Iscar ◽  
J. Ortega
Keyword(s):  
Cluster Analysis ◽  
Robust Clustering

The influence function of the TCLUST robust clustering procedure

2012 ◽  
Vol 6 (2) ◽  
pp. 107-130 ◽  
Author(s):  
C. Ruwet ◽  
L. A. García-Escudero ◽  
A. Gordaliza ◽  
A. Mayo-Iscar
Keyword(s):  
Influence Function ◽  
Robust Clustering

scholarly journals TCLUST: Trimming Approach of Robust Clustering Method

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Muhamad Alias Md. Jedi ◽  
Robiah Adnan
Keyword(s):  
Clustering Algorithm ◽  
Likelihood Function ◽  
R Package ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Robust Clustering ◽  
Scatter Matrix ◽  
Group Assignment ◽  
Log Likelihood ◽  
Clustering Approach

TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-means clustering algorithm. It is called “crisp” clustering approach because the observation is can be eliminated or assigned to a group. TCLUST strengthen the group assignment by putting constraint to the cluster scatter matrix. The emphasis in this paper is to restrict on the eigenvalues, λ of the scatter matrix. The idea of imposing constraints is to maximize the log-likelihood function of spurious-outlier model. A review of different robust clustering approach is presented as a comparison to TCLUST methods. This paper will discuss the nature of TCLUST algorithm and how to determine the number of cluster or group properly and measure the strength of group assignment. At the end of this paper, R-package on TCLUST implement the types of scatter restriction, making the algorithm to be more flexible for choosing the number of clusters and the trimming proportion.

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