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Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool

G.G.Gokilam 1 , K.Shanthi 2

Section:Review Paper, Product Type: Journal Paper
Volume-3 , Issue-2 , Page no. 1-5, Feb-2015

Online published on Feb 28, 2015

Copyright © G.G.Gokilam , K.Shanthi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: G.G.Gokilam , K.Shanthi, “Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.1-5, 2015.

MLA Style Citation: G.G.Gokilam , K.Shanthi "Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool." International Journal of Computer Sciences and Engineering 3.2 (2015): 1-5.

APA Style Citation: G.G.Gokilam , K.Shanthi, (2015). Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool. International Journal of Computer Sciences and Engineering, 3(2), 1-5.

BibTex Style Citation:
@article{_2015,
author = {G.G.Gokilam , K.Shanthi},
title = {Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2015},
volume = {3},
Issue = {2},
month = {2},
year = {2015},
issn = {2347-2693},
pages = {1-5},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=392},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=392
TI - Comparing clustering Algorithms with Diabetic Datasets in WEKA Tool
T2 - International Journal of Computer Sciences and Engineering
AU - G.G.Gokilam , K.Shanthi
PY - 2015
DA - 2015/02/28
PB - IJCSE, Indore, INDIA
SP - 1-5
IS - 2
VL - 3
SN - 2347-2693
ER -

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Abstract

Data mining is the process of discover useful information from large datasets. The data mining techniques are used to analyze and evaluate diabetic dataset in the field of bio-medical. One of the most important techniques of data mining is clustering which is used to analyzing data from different perspectives and summarizing into useful information. Clustering is the task of assigning a set of objects into group called clusters. This paper discusses different clustering algorithms like cobweb, DBSCAN, EM, Farthest first, filtered cluster hierarchical cluster, OPTICS, simple Kmeans. The algorithms are used to compare its performance by Time taken to build the clusters, the cluster differentiated by its true positive and true negative values. Our main aim to show the comparison of the different cluster algorithms are evaluated in weka tool (Data mining Tool) and find out which algorithm will be most suitable for the diabetes dataset.

Key-Words / Index Term

Cluster, Diabetes , Weka ,Data Mining

References

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