This paper is published in Volume-3, Issue-2, 2017
Area
Parallel Computing
Author
Ghanshyam Dewta, Rajesh Tiwari
Org/Univ
SSTC FET, Bhilai, India
Pub. Date
24 April, 2017
Paper ID
V3I2-1357
Publisher
Keywords
Clustering, DBSCAN, Data Mining, Parallel Processing Languages.

Citationsacebook

IEEE
Ghanshyam Dewta, Rajesh Tiwari. A Distributed Approach for the Development of the DBSCAN Clustering, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Ghanshyam Dewta, Rajesh Tiwari (2017). A Distributed Approach for the Development of the DBSCAN Clustering. International Journal of Advance Research, Ideas and Innovations in Technology, 3(2) www.IJARIIT.com.

MLA
Ghanshyam Dewta, Rajesh Tiwari. "A Distributed Approach for the Development of the DBSCAN Clustering." International Journal of Advance Research, Ideas and Innovations in Technology 3.2 (2017). www.IJARIIT.com.

Abstract

Clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, web analysis, bioinformatics, and many others DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. The key idea of the DBSCAN algorithm is that for each data point in a cluster, the neighborhood within a given radius has to contain at least a minimum number of points. We have proposed improved evenhanded workload allocation using hierarchical (Tree-Based) approach for constructing of data cluster
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