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Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner

by Tanu Verma, Renu, Deepti Gaur
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 2
Year of Publication: 2014
Authors: Tanu Verma, Renu, Deepti Gaur
10.5120/ijais14-451141

Tanu Verma, Renu, Deepti Gaur . Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner. International Journal of Applied Information Systems. 7, 2 ( April 2014), 22-24. DOI=10.5120/ijais14-451141

@article{ 10.5120/ijais14-451141,
author = { Tanu Verma, Renu, Deepti Gaur },
title = { Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner },
journal = { International Journal of Applied Information Systems },
issue_date = { April 2014 },
volume = { 7 },
number = { 2 },
month = { April },
year = { 2014 },
issn = { 2249-0868 },
pages = { 22-24 },
numpages = {9},
url = { https://www.ijais.org/archives/volume7/number2/622-1141/ },
doi = { 10.5120/ijais14-451141 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:54:39.639372+05:30
%A Tanu Verma
%A Renu
%A Deepti Gaur
%T Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 7
%N 2
%P 22-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clustering methods can be categorized into two main types: fuzzy clustering and hard clustering. In fuzzy clustering, data points can belong to more than one cluster with probabilities. In hard clustering, data points are divided into distinct clusters, where each data point can belong to one and only one cluster[1]. In this paper, we have calculated similarity measure in Rapid miner.

References
  1. J. , Data Mining Concepts and Techniques. Kaufman, 2006
  2. Margaret H. Dunham, Data Mining "Introduction and Advanced Topics".
  3. IEEE Trans Xu R. Survey of clustering algorithms. Neural Networks 2005;16.
  4. Anant Ram, Sunita Jalal, Anand S. Jalal, Manoj kumar, "A density Based Algorithm for Discovery Density Varied cluster in Large spatial Databases", International Journal of Computer Application Volume 3,No. 6, June 2010.
  5. Derya Birant, Alp Kut, "ST-DBSCAN: An Algorithm for Clustering Spatial-temporal data" Data and Knowledge Engineering 2007 pg 208-221.
  6. Peng Liu, Dong Zhou, Naijun Wu," Varied Density Based Sp6ial Clustering of Application with Noise", in proceedings of IEEE Conference ICSSSM 2007 pg 528-531.
  7. A. K. M Rasheduzzaman Chowdhury, Md. Asikur Rahman, "An efficient Mehtod for subjectively choosing parameter k automatically in VDBSCAN",proceedings of ICCAE 2010 IEEE ,Vol 1,pg 38-41.
  8. Mohammad N. T. Elbatta, An improvement of DBSCAN algorithm for best results in varied densities.
Index Terms

Computer Science
Information Sciences

Keywords

DBSCAN similarity measure

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