scholarly journals Comparative Study of Distribution Clustering Methods Based on Density-Difference Estimation

2017 ◽  
Vol 29 (5) ◽  
pp. 651-654
Author(s):  
Syo NISHIHARA ◽  
Tomoharu NAKASHIMA
Keyword(s):  
Comparative Study ◽  
Density Difference ◽  
Clustering Methods

A Comparative Study on Text Clustering Methods

2006 ◽  
pp. 644-651 ◽  
Author(s):  
Yan Zheng ◽  
Xiaochun Cheng ◽  
Ronghuai Huang ◽  
Yi Man
Keyword(s):  
Comparative Study ◽  
Text Clustering ◽  
Clustering Methods

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.

A Comparative Study of Graph Kernels and Clustering Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 33-48
Author(s):  
Riju Bhattacharya ◽  
Naresh Kumar Nagwani ◽  
Sarsij Tripathi
Keyword(s):  
Random Walk ◽  
Comparative Study ◽  
Shortest Path ◽  
Kernel Method ◽  
Graph Clustering ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Graph Kernels ◽  
Silhouette Index

Graph kernels have evolved as a promising and popular method for graph clustering over the last decade. In this work, comparative study on the five standard graph kernel techniques for graph clustering has been performed. The graph kernels, namely vertex histogram kernel, shortest path kernel, graphlet kernel, k-step random walk kernel, and Weisfeiler-Lehman kernel have been compared for graph clustering. The clustering methods considered for the kernel comparison are hierarchical, k-means, model-based, fuzzy-based, and self-organizing map clustering techniques. The comparative study of kernel methods over the clustering techniques is performed on MUTAG benchmark dataset. Clustering performance is assessed with internal validation performance parameters such as connectivity, Dunn, and the silhouette index. Finally, the comparative analysis is done to facilitate researchers for selecting the appropriate kernel method for effective graph clustering. The proposed methodology elicits k-step random walk and shortest path kernel have performed best among all graph clustering approaches.

scholarly journals A Design of Experiments Comparative Study on Clustering Methods

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167726-167738 ◽  
Author(s):  
Natalia Maria Puggina Bianchesi ◽  
Estevao Luiz Romao ◽  
Marina Fernandes B. P. Lopes ◽  
Pedro Paulo Balestrassi ◽  
Anderson Paulo De Paiva
Keyword(s):  
Design Of Experiments ◽  
Comparative Study ◽  
Clustering Methods

A comparative study of clustering methods using word embeddings

2019 ◽  
Author(s):  
Nikolaos Bastas ◽  
George Kalpakis ◽  
Theodora Tsikrika ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris
Keyword(s):  
Comparative Study ◽  
Word Embeddings ◽  
Clustering Methods
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