scholarly journals A Comparative Study on Clustering Algorithms using Image Data

2016 ◽  
Vol 133 (17) ◽  
pp. 28-31
Author(s):  
Vikas Tondar ◽  
Pramod Nair
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Image Data

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.

Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

2011 ◽  
Vol 474-476 ◽  
pp. 442-447
Author(s):  
Zhi Gao Zeng ◽  
Li Xin Ding ◽  
Sheng Qiu Yi ◽  
San You Zeng ◽  
Zi Hua Qiu
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Mixture Models ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Image Data ◽  
Gaussian Mixture ◽  
Parameters Estimation ◽  
Video Image

In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

scholarly journals A Comparative Study of Data Clustering Algorithms

2013 ◽  
Vol 83 (15) ◽  
pp. 41-46 ◽  
Author(s):  
Geet Singhal ◽  
Shipra Panwar ◽  
Kanika Jain ◽  
Devender Banga
Keyword(s):  
Comparative Study ◽  
Data Clustering ◽  
Clustering Algorithms
Sign in / Sign up

Export Citation Format

Share Document