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Applications of Cluster Analysis

T. Aruna1

Section:Research Paper, Product Type: Journal Paper
Volume-07 , Issue-17 , Page no. 19-21, May-2019

Online published on May 22, 2019

Copyright © T. Aruna . 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: T. Aruna, “Applications of Cluster Analysis,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.17, pp.19-21, 2019.

MLA Style Citation: T. Aruna "Applications of Cluster Analysis." International Journal of Computer Sciences and Engineering 07.17 (2019): 19-21.

APA Style Citation: T. Aruna, (2019). Applications of Cluster Analysis. International Journal of Computer Sciences and Engineering, 07(17), 19-21.

BibTex Style Citation:
@article{Aruna_2019,
author = {T. Aruna},
title = {Applications of Cluster Analysis},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {17},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {19-21},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1301},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=1301
TI - Applications of Cluster Analysis
T2 - International Journal of Computer Sciences and Engineering
AU - T. Aruna
PY - 2019
DA - 2019/05/22
PB - IJCSE, Indore, INDIA
SP - 19-21
IS - 17
VL - 07
SN - 2347-2693
ER -

           

Abstract

Clustering is the process of grouping the data into classes or clusters, so those objects with in a cluster have high similarity in comparison to one another but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on attribute values describing the objects. Clustering has its roots in many areas like data mining, statistics, biology and machine learning. We examine several clustering techniques organized into the following categories partitioning methods, hierarchical method, density based method, grid- based method, model-based method, frequent pattern based method and constraint clustering.

Key-Words / Index Term

Cluster, Matrix, Algorithm

References

[1] L. Jawadeekar, “ Data Mining concepts and Techniques”, Tata McGraw-Hill Publication, India,
[2] Jiawei han, Micheline Kamber, Jian pei “ Data Mining concepts and Techniques”, Publisher: Morgan Kaufman,
[3] Hand Amber pei i “ Data Mining concepts and Techniques”, Second Edition
[4] S. Mythili, E.Madhaiya,“An Analysis on Clustering Algorithms in Data mining”, IJCSMC, Vol. 3, Issue. 1, January 2014, pg.334 – 340.