A comparative study of DIGNET, average, complete, single hierarchical and k-means clustering algorithms in 2D face image recognition

2014 ◽  
Author(s):  
Konstantinos-Georgios Thanos ◽  
Stelios C. A. Thomopoulos
Keyword(s):  
Comparative Study ◽  
Image Recognition ◽  
Clustering Algorithms ◽  
Face Image

New Techniques for Face Image Recognition Identification and Labeling

2007 ◽  
Vol 1 (4) ◽  
pp. 62-69
Author(s):  
Milhled Alfaouri ◽  
◽  
Nada N. Al-Ramahi ◽  
Keyword(s):  
Image Recognition ◽  
Face Image ◽  
New Techniques

Performance Evaluation Of Face Image Recognition Based Voila-Joins With SVM

2019 ◽  
pp. 161
Author(s):  
Jamal Mustafa Al-Tuwaijari ◽  
Suhad Ibrahim Mohammed
Keyword(s):  
Performance Evaluation ◽  
Image Recognition ◽  
Face Image

A Comparative Study of Hard Clustering Algorithms for Vegetation Data

2021 ◽  
Author(s):  
Naghmeh Pakgohar ◽  
Javad Eshaghi Rad ◽  
Gholam Hossein Gholami ◽  
Ahmad Alijanpour ◽  
David W. Roberts
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Hard Clustering

Data Clustering Algorithms Using Rough Sets

2013 ◽  
pp. 297-327 ◽  
Author(s):  
B.K. Tripathy ◽  
Adhir Ghosh
Keyword(s):  
Comparative Study ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Future Studies ◽  
Multiple Clusters

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to handle categorical data. In order to handle the situations where objects can have memberships in multiple clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a). There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004; Peters 2006, 2007). The MMR algorithm (Parmar et al 2007), its extensions (Tripathy et al 2009, 2011a, 2011b) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also present problems for future studies in the direction of the topics covered.

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