Manuscript Number : IJSRSET1844213
Enhancing of DBSCAN by Using Optics Algorithm in Data Mining
Authors(2) :-Y. Vijay Bhaskar Reddy, Dr. L. S. S. Reddy
DBSCAN is Partitional type clustering method. Here, more dense regions are considered as clusters and remaining area is called noise. The cluster is defined on some components like noise, core region and border. DBSCAN is resistant to noise. It can handle different clusters with various sizes and shapes. There are a lot of clusters that DBSCAN can discover which are unable to find by using K-Means clustering algorithm. But, DBSCAN does not work well when we deal with clusters of ‘varying densities’ and ‘high dimensional data’. It is sensitive to clustering parameters like MinPts and Eps values. To overcome this we are using OPTICS technique. DBSCAN technique takes high time for formation of clustering. To enhancing this; we are discussing about OPTICS clustering algorithms.
Y. Vijay Bhaskar Reddy
Density based clustering, DBSCAN, OPTICS, Connectivity, Datasets.
Publication Details
Published in :
Volume 4 | Issue 1 | January-February 2018 Article Preview
Research Scholar, Rayalaseema University, Kurnool, Andhra Pradesh, India
Dr. L. S. S. Reddy
Vice Chancellor, KL University, Vaddeswaram. Guntur, Andhra Pradesh, India
Date of Publication :
2018-01-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
1572-1576
Manuscript Number :
IJSRSET1844213
Publisher : Technoscience Academy
Journal URL :
https://ijsrset.com/IJSRSET1844213