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Quality Cluster Generation Using Random Projections

P.A. Gat1 , K.S. Kadam2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 933-936, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.933936

Online published on Jun 30, 2019

Copyright © P.A. Gat, K.S. Kadam . 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: P.A. Gat, K.S. Kadam, “Quality Cluster Generation Using Random Projections,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.933-936, 2019.

MLA Style Citation: P.A. Gat, K.S. Kadam "Quality Cluster Generation Using Random Projections." International Journal of Computer Sciences and Engineering 7.6 (2019): 933-936.

APA Style Citation: P.A. Gat, K.S. Kadam, (2019). Quality Cluster Generation Using Random Projections. International Journal of Computer Sciences and Engineering, 7(6), 933-936.

BibTex Style Citation:
@article{Gat_2019,
author = {P.A. Gat, K.S. Kadam},
title = {Quality Cluster Generation Using Random Projections},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {933-936},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4657},
doi = {https://doi.org/10.26438/ijcse/v7i6.933936}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.933936}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4657
TI - Quality Cluster Generation Using Random Projections
T2 - International Journal of Computer Sciences and Engineering
AU - P.A. Gat, K.S. Kadam
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 933-936
IS - 6
VL - 7
SN - 2347-2693
ER -

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Abstract

Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. Clusters are obtained by using density based clustering and DBSCAN clustering. DBSCAN cluster is a fast clustering technique, large complexity and requires large parameters. To overcome of these problems uses the OPTICS density based algorithm. The algorithm requires the simply a single parameter, namely the least amount of points in a cluster which is required as input in density based technique. Using random projection improving the cluster quality and run time.

Key-Words / Index Term

Cluster Analysis, Random Projections, Neighbouring

References

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