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Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner

Tanu Verma, Renu, Deepti Gaur Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
10.5120/ijais14-451141
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  1. Tanu Verma, Renu and Deepti Gaur. Article: Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner. International Journal of Applied Information Systems 7(2):22-24, April 2014. BibTeX

    @article{key:article,
    	author = "Tanu Verma and Renu and Deepti Gaur",
    	title = "Article: Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 7,
    	number = 2,
    	pages = "22-24",
    	month = "April",
    	note = "Published by Foundation of Computer Science, New York, 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.

Reference

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  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.

Keywords

DBSCAN, similarity measure,