Partitional Clustering Techniques for Multi-Spectral Image Segmentation

2007 ◽  
Vol 2 (10) ◽  
Author(s):  
Danielle Nuzillard ◽  
Cosmin Lazar
Keyword(s):  
Image Segmentation ◽  
Spectral Image ◽  
Clustering Techniques ◽  
Partitional Clustering

Clustering Techniques and Their Applications: A Review

2020 ◽  
Vol 1 (4) ◽  
pp. 1-6
Author(s):  
Arjun Dutta
Keyword(s):  
Data Mining ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Partitional Clustering ◽  
Gene Information ◽  
Scientific Fields

This paper deals with concise study on clustering: existing methods and developments made at various times. Clustering is defined as an unsupervised learning where the targets are sorted out on the foundation of some similarity inherent among them. In the recent times, we dispense with large masses of data including images, video, social text, DNA, gene information, etc. Data clustering analysis has come out as an efficient technique to accurately achieve the task of categorizing information into sensible groups. Clustering has a deep association with researches in several scientific fields. k-means algorithm was suggested in 1957. K-mean is the most popular partitional clustering method till date. In many commercial and non-commercial fields, clustering techniques are used. The applications of clustering in some areas like image segmentation, object and role recognition and data mining are highlighted. In this paper, we have presented a brief description of the surviving types of clustering approaches followed by a survey of the areas.

Hyper-spectral image segmentation using an improved PSO aided with multilevel fuzzy entropy

2019 ◽  
Vol 78 (23) ◽  
pp. 34027-34063 ◽  
Author(s):  
Rupak Chakraborty ◽  
Rama Sushil ◽  
M. L. Garg
Keyword(s):  
Image Segmentation ◽  
Fuzzy Entropy ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Improved Pso

Comprehensive Study and Analysis of Partitional Data Clustering Techniques

2015 ◽  
Vol 2 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Aparna K. ◽  
Mydhili K. Nair
Keyword(s):  
Data Clustering ◽  
Evolutionary Programming ◽  
Technical Aspect ◽  
Mixture Modeling ◽  
Crime Analysis ◽  
Clustering Techniques ◽  
Partitional Clustering ◽  
New Development ◽  
Computational Requirement ◽  
Comprehensive Study

Data clustering has found significant applications in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. There are several types of data clustering such as partitional, hierarchical, spectral, density-based, mixture-modeling to name a few. Among these, partitional clustering is well suited for most of the applications due to the less computational requirement. An analysis of various literatures available on partitional clustering will not only provide good knowledge, but will also lead to find the recent problems in partitional clustering domain. Accordingly, it is planned to do a comprehensive study with the literature of partitional data clustering techniques. In this paper, thirty three research articles have been taken for survey from the standard publishers from 2005 to 2013 under two different aspects namely the technical aspect and the application aspect. The technical aspect is further classified based on partitional clustering, constraint-based partitional clustering and evolutionary programming-based clustering techniques. Furthermore, an analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in partitional data clustering can be made easier to be carried out by researchers.

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