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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2011 - 13

Application of Rosette Pattern for Clustering and Determining the Number of Cluster

SADR, A. See more information about SADR, A. on SCOPUS See more information about SADR, A. on IEEExplore See more information about SADR, A. on Web of Science, MOMTAZ, A. K. See more information about MOMTAZ, A. K. on SCOPUS See more information about MOMTAZ, A. K. on SCOPUS See more information about MOMTAZ, A. K. on Web of Science
 
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Download PDF pdficon (2,657 KB) | Citation | Downloads: 792 | Views: 2,726

Author keywords
clustering, Fuzzy C-means (FCM), pattern recognition, Rosette Pattern, validity index

References keywords
clustering(17), pattern(12), fuzzy(11), algorithms(8), recognition(7), data(7), analysis(7), rosette(5), hall(5), clusters(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-08-31
Volume 11, Issue 3, Year 2011, On page(s): 77 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.03013
Web of Science Accession Number: 000296186700013
SCOPUS ID: 80055116504

Abstract
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Clustering is one of the most important research topics which has many practical applications such as medical imaging and Non-Destructive Testing (NDT). Most clustering algorithms like K-means, fuzzy C-Means (FCM) and their derivatives require the number of clusters as one of the initializing parameters. This paper proposes an algorithm for image clustering with no need to any initializing parameter. In this state-of-the-art, an image is sampled based on a rosette pattern and according to the pattern characteristics, the extracted samples are clustered and then the number of clusters is determined. The centroids of classes are computed by means of a method based on calculation of distribution function. Based on different data sets, the results show that the algorithm improves the capability of the clustering by a minimum of 62.26% and 87.62% in comparison with FCM and K-means algorithms, respectively. Moreover, in dealing with high resolution data sets, the efficiency of the algorithm in clusters detection and run time improvement increases considerably.


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References Weight

Web of Science® Citations for all references: 8,922 TCR
SCOPUS® Citations for all references: 19,788 TCR

Web of Science® Average Citations per reference: 218 ACR
SCOPUS® Average Citations per reference: 483 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-02-24 12:37 in 123 seconds.




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