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Reseach Article

Data Mining: An experimental approach with WEKA on UCI Dataset

by Ajay Kumar, Indranath Chatterjee
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 13
Year of Publication: 2016
Authors: Ajay Kumar, Indranath Chatterjee
10.5120/ijca2016909050

Ajay Kumar, Indranath Chatterjee . Data Mining: An experimental approach with WEKA on UCI Dataset. International Journal of Computer Applications. 138, 13 ( March 2016), 23-28. DOI=10.5120/ijca2016909050

@article{ 10.5120/ijca2016909050,
author = { Ajay Kumar, Indranath Chatterjee },
title = { Data Mining: An experimental approach with WEKA on UCI Dataset },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 13 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number13/24441-2016909050/ },
doi = { 10.5120/ijca2016909050 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:37.724213+05:30
%A Ajay Kumar
%A Indranath Chatterjee
%T Data Mining: An experimental approach with WEKA on UCI Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 13
%P 23-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining became a popular research field these days. The reasons that attracted attention in information technology, the discovery of meaningful information from large collections of data. Data mining is the perception that we are data rich but very much information poor. Large amount of data is available all around but we can hardly able to turn them in to useful information. The comparative analysis of available classification and clustering algorithms is provided in this paper through theoretical and practical approach with WEKA tool. It also includes the future directions for researchers in the field of data mining.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Weka Classification Clustering UCI dataset.