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To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review
To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review
P.K. Dhillon1 , A.S. Walia2

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-3 , Page no. 35-39, Mar-2017

Online published on Mar 31, 2017

Copyright © P.K. Dhillon, A.S. Walia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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IEEE Style Citation: P.K. Dhillon, A.S. Walia, “To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.35-39, 2017.

MLA Style Citation: P.K. Dhillon, A.S. Walia "To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review." International Journal of Computer Sciences and Engineering 5.3 (2017): 35-39.

APA Style Citation: P.K. Dhillon, A.S. Walia, (2017). To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review. International Journal of Computer Sciences and Engineering, 5(3), 35-39.
           
Abstract :
There is ample amount of data present in the whole world. The data is generated from various sources like companies, organizations, social networking sites, image processing, world wide web, scientific and medical etc. People have less time to look at whole data. They attended towards the precious and interested information. Data mining is technique which is used to extract meaningful information from huge databases. Extracted information is visualized in the form of statics, graphs, and tables and videos etc. There are number of data mining techniques, asymmetric clustering is one of them. Asymmetric technique is type of unsupervised learning. In this, data sets which have similarity are placed in one cluster and others are in different clusters. From, number of years various asymmetric clustering techniques are introduced which work well with datasets. These techniques do not work well with the complex and strongly coupled data sets. To reduce processing time and improve accuracy neural networks are combined with asymmetric clustering algorithms.
Key-Words / Index Term :
Backpropagation, Data mining, DBSCAN, neural network, normalization
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