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Centroid Update Approach to K-Means ClusteringBORLEA, I.-D. , PRECUP, R.-E. , DRAGAN, F. , BORLEA, A.-B.
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clustering algorithms, clustering methods, data analysis, data mining, machine learning algorithms
data(12), fuzzy(9), algorithms(9), systems(7), control(7), comput(7), optimal(6), clustering(6), algorithm(6), system(5)
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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
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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