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Statistics report
Aug. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 113
Paper Published : 1372
No. of Authors : 3469
  Journal Paper


Paper Title :
Accurate Algorithm for Unsupervised Learning in Large Data Sets

Author :Syed Quddus, Adil M. Bagirov

Article Citation :Syed Quddus ,Adil M. Bagirov , (2021 ) " Accurate Algorithm for Unsupervised Learning in Large Data Sets " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 22-25, Volume-9,Issue-4

Abstract : Unsupervised learning or clustering in large data sets is a challenging problem. Most clustering algorithms are not efficient and accurate in such data sets. Therefore development of clustering algorithms capable of solving clustering problems in large data sets is very important. In this paper, we consider one such accurate algorithm and test it using large data sets. Our algorithm is based on the nonsmooth optimization formulation of the clustering problem. In this problem we use the squared Euclidean norm to define the similarity measure and apply the difference of convex representation of the clustering function. Keywords - Cluster Analysis; Data Mining; Algorithms.

Type : Research paper

Published : Volume-9,Issue-4


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