Quality Cluster Generation Using Random Projections

Authors

  • PA Gat Department of Computer Science, DKTE’s College, Ichalkaranji, India
  • KS Kadam Department of Computer Science, DKTE’s College, Ichalkaranji, India

DOI:

https://doi.org/10.26438/ijcse/v7i6.933936

Keywords:

Cluster Analysis, Random Projections, Neighbouring

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.

References

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Published

2019-06-30
CITATION
DOI: 10.26438/ijcse/v7i6.933936
Published: 2019-06-30

How to Cite

[1]
G. PA and K. Kadam, “Quality Cluster Generation Using Random Projections”, Int. J. Comp. Sci. Eng., vol. 7, no. 6, pp. 933–936, Jun. 2019.

Issue

Vol. 7 No. 6 (2019): IJCSE June Edition

Section

Research Article

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