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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2017 - 1
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 HIGH-IMPACT PAPER 

Centroid Update Approach to K-Means Clustering

BORLEA, I.-D. See more information about BORLEA, I.-D. on SCOPUS See more information about BORLEA, I.-D. on IEEExplore See more information about BORLEA, I.-D. on Web of Science, PRECUP, R.-E. See more information about  PRECUP, R.-E. on SCOPUS See more information about  PRECUP, R.-E. on SCOPUS See more information about PRECUP, R.-E. on Web of Science, DRAGAN, F. See more information about  DRAGAN, F. on SCOPUS See more information about  DRAGAN, F. on SCOPUS See more information about DRAGAN, F. on Web of Science, BORLEA, A.-B. See more information about BORLEA, A.-B. on SCOPUS See more information about BORLEA, A.-B. on SCOPUS See more information about BORLEA, A.-B. on Web of Science
 
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Download PDF pdficon (1,203 KB) | Citation | Downloads: 2,326 | Views: 3,494

Author keywords
clustering algorithms, clustering methods, data analysis, data mining, machine learning algorithms

References keywords
data(12), fuzzy(9), algorithms(9), systems(7), control(7), comput(7), optimal(6), clustering(6), algorithm(6), system(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04001
Web of Science Accession Number: 000417674300001
SCOPUS ID: 85035816652

Abstract
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The volume and complexity of the data that is generated every day increased in the last years in an exponential manner. For processing the generated data in a quicker way the hardware capabilities evolved and new versions of algorithms were created recently, but the existing algorithms were improved and even optimized as well. This paper presents an improved clustering approach, based on the classical k-means algorithm, and referred to as the centroid update approach. The new centroid update approach formulated as an algorithm and included in the k-means algorithm reduces the number of iterations that are needed to perform a clustering process, leading to an alleviation of the time needed for processing a dataset.


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