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JCR Impact Factor: 0.699
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  4/2017 - 1
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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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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

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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.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 8,401 TCR
SCOPUS® Citations for all references: 11,697 TCR

Web of Science® Average Citations per reference: 215 ACR
SCOPUS® Average Citations per reference: 300 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 2019-04-22 02:03 in 160 seconds.

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