2026-09-17T06:21:38Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1433152025-06-05T12:36:20Zcom_10366_142734com_10366_122682com_10366_4666com_10366_3823col_10366_143230
Gestión del Repositorio Documental de la Universidad de Salamanca
author
Ullah, Rafi
author
Khan, Ayaz H.
author
Emaduddin, S.m.
2020-06-23T11:12:50Z
2020-06-23T11:12:50Z
2019-08-14
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8 (2019)
2255-2863
http://hdl.handle.net/10366/143315
k-Nearest Neighbor (k-NN) is a non-parametric algorithm widely used for the estimation and classification of data points especially when the dataset is distributed in several classes. It is considered to be a lazy machine learning algorithm as most of the computations are done during the testing phase instead of performing this task during the training of data. Hence it is practically inefficient, infeasible and inapplicable while processing huge datasets i.e. Big Data. On the other hand, clustering techniques (unsupervised learning) greatly affect results if you do normalization or standardization techniques, difficult to determine "k" Value. In this paper, some novel techniques are proposed to be used as pre-state mechanism of state-of-the-art k-NN Classification Algorithm. Our proposed mechanism uses unsupervised clustering algorithm on large dataset before applying k-NN algorithm on different clusters that might running on single machine, multiple machines or different nodes of a cluster in distributed environment. Initially dataset, possibly having multi dimensions, is pass through clustering technique (K-Means) at master node or controller to find the number of clusters equal to the number of nodes in distributed systems or number of cores in system, and then each cluster will be assigned to exactly one node or one core and then applies k-NN locally, each core or node in clusters sends their best result and the selector choose best and nearest possible class from all options. We will be using one of the gold standard distributed framework. We believe that our proposed mechanism could be applied on big data. We also believe that the architecture can also be implemented on multi GPUs or FPGA to take flavor of k-NN on large or huge datasets where traditional k-NN is very slow.
eng
info:eu-repo/semantics/openAccess
Computación
Informótica
Computing
Information Technology
ck-NN: A Clustered k-Nearest Neighbours Approach for Large-Scale Classification
info:eu-repo/semantics/article
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ZXN1cG9uZSBxdWUgc2UgaGEgY3VtcGxpZG8gY29uIGN1YWxxdWllciBkZXJlY2hvIGRlIHJldmlzacOzbiB1IG90cmFzIG9ibGlnYWNpb25lcyByZXF1ZXJpZGFzIHBvciBlc3RlIGNvbnRyYXRvIG8gYWN1ZXJkby4KCjcuTGEgVW5pdmVyc2lkYWQgZGUgU2FsYW1hbmNhIGlkZW50aWZpY2Fyw6EgY2xhcmFtZW50ZSBzdS9zIG5vbWJyZS9zIGNvbW8gZWwvbG9zIGF1dG9yL2VzIG8gcHJvcGlldGFyaW8vcyBkZSBsb3MgZGVyZWNob3MgZGVsIGRvY3VtZW50bywgeSBubyBoYXLDoSBuaW5ndW5hIGFsdGVyYWNpw7NuIGRlIHN1IGRvY3VtZW50byBkaWZlcmVudGUgYSBsYXMgcGVybWl0aWRhcyBlbiBlc3RhIGxpY2VuY2lhLg==
URL
https://gredos.usal.es/bitstream/10366/143315/1/ck-NN_A_Clustered_k-Nearest_Neighbours_A.pdf
File
MD5
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application/pdf
ck-NN_A_Clustered_k-Nearest_Neighbours_A.pdf
URL
https://gredos.usal.es/bitstream/10366/143315/5/ck-NN_A_Clustered_k-Nearest_Neighbours_A.pdf.txt
File
MD5
ce8c9c9242440bb4411faa8a5757d936
32760
text/plain
ck-NN_A_Clustered_k-Nearest_Neighbours_A.pdf.txt