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An Experimental Survey on Single Linkage Clustering

by Krishna K. Mohbey, G. S. Thakur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 17
Year of Publication: 2013
Authors: Krishna K. Mohbey, G. S. Thakur
10.5120/13337-0327

Krishna K. Mohbey, G. S. Thakur . An Experimental Survey on Single Linkage Clustering. International Journal of Computer Applications. 76, 17 ( August 2013), 6-11. DOI=10.5120/13337-0327

@article{ 10.5120/13337-0327,
author = { Krishna K. Mohbey, G. S. Thakur },
title = { An Experimental Survey on Single Linkage Clustering },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 17 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number17/13337-0327/ },
doi = { 10.5120/13337-0327 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:48:38.921994+05:30
%A Krishna K. Mohbey
%A G. S. Thakur
%T An Experimental Survey on Single Linkage Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 17
%P 6-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clusters are useful to identify required object from the huge amount of datasets. There are lots of clustering methods, used to create clusters. Single linkage clustering method is an example of hierarchical agglomerative clustering which is used to merge objects in a cluster, based on minimum distance. In this paper we performed an experiment on two dimensional spaces where multiple objects are available and combine in clusters by Euclidean distance. In this paper, MATLAB is used to calculate the distance between two objects and constructing distance matrix. After completing the whole single linage clustering method dendogram has been prepared. This dendogram is similar to minimum spanning tree because it is prepared using minimum distance of objects. These prepared clusters and dendogram are useful for finding different knowledge from the huge data.

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Index Terms

Computer Science
Information Sciences

Keywords

Single Link clustering Similarity measurements Dendogram

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