Extended Advanced Method of Clustering Big data to achieve high dimensionality
N.Sree Ram1, M.H.M.Krishna Prasad2, K.Satya Prasad3
1N.SREERAM*, Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India Research Scholar JNTUK, Kakinada A.P, India.
2Dr.M.H.M. Krishna Prasad, Professor of CSE, Vice-Principal & Coordinator-TEQIP-III,, University College of Engineering Kakinada(A) J.N.T.U. Kakinada.
3Dr.kK. Satya Prasad, Professor of ECE (Rtd), ,, University College of Engineering Kakinada(A) J.N.T.U. Kakinada.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 751-757 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7108118419/2019©BEIESP | DOI: 10.35940/ijrte.D7108.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering is one of the relevant knowledge engineering methods of data analysis. The clustering method will automatically directly affect the result dataset. The proposed work aims at developing an Extended Advanced Method of Clustering (EAMC) to address numerous types of issues associated to large and high dimensional dataset. The proposed Extended Advance Method of clustering will repetitively avoid computational time between each data cluster object contained by the cluster that saves execution time in term. For each iteration EAMC needs a data structure to store data that can be utilized for the next iteration. We have gained outcomes from the proposed method, which demonstrates that there is an improvement in effectiveness and pace of clustering and precision generation, which will decrease the convolution of computing over the old algorithms like SOM, HAC, and K-means. This paper includes EAMC and the investigational outcomes done using academic datasets.
Keywords: Convolution Of Computing, Extended Advanced Method, High Dimensional Dataset, HAC, Pace Of Clustering, Precision Generation, And SOM.
Scope of the Article: Clustering.