Open AccessCentroid Update Approach to K-Means Clustering
Author(s) -
Ioan-Daniel Borlea,
RaduEmil Precup,
Florin Drăgan,
Alexandra-Bianca Borlea
Publication year - 2017
Publication title -
advances in electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 23
eISSN - 1844-7600
pISSN - 1582-7445
DOI - 10.4316/aece.2017.04001
Subject(s) - cluster analysis , centroid , computer science , volume (thermodynamics) , exponential function , data mining , artificial intelligence , mathematics , mathematical analysis , physics , quantum mechanics
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