|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.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
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.
|150||128 downloads||80 downloads|
|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|
 R. Buyyr, J. broberg, A. Goscinski, “Cloud Computing Principlesand Paradigms”, John Wiley & Sons,Inc publications , pp. 63-65, 2011.
 H.J. Jiawei, M. Kamber, “Data Mining: Concepts and Techniques (3rd ed.)”. Morgan Kaufmann publication, San Francisco, pp. 26-39, 2012.
 A. Nagpal,. A. Jatain, D. Gaur, “Review based on data clustering algorithms”, in: Proceedings of 2013 IEEE International Conference on Information and Communication Technologies (ICT 2013), pp. 171–176, 2013.
 W. Yu, G. Qiang, L. Xiao-Li, “A kernel aggregate clustering approach for mixed data set and its application in customer segmentation”, in: International Conference on Management Science and Engineering ICMSE, , pp. 121–124, 2006.
 Dr.R. kishore, T.Kaur, “Backpropagation Algorithm: An Artificial Neural Network Approach for Pattern Recognition” in: International Journel of Scientific and Engineering Research, Vol. 3(6), pp. 1-4, 2012.
 K. Vora, S. Yagnik, “A Survey on Backpropagation Algorithms for Feedforward Neural Networks” in: International Journal of Engineering Development and Research(IJEDR), pp. 191-197, 2010.
 Z. Nafar, A. Golshani, “Data mining methods for protein–protein interactions”, in: Canadian Conference on Electrical and Computer Engineering, CCECE, pp. 991–994, 2006.
 A.M. Bakr, N.A. Yousri, M.A. Ismail, “Efficient incremental phrase-based document clustering”, in: International Conference on Pattern Recognition ICPR, Tsukuba (Japan), pp. 517–520, 2012.
 S. Nithyakalyani, S.S. Kumar, “Data aggregation in wireless sensor network using node clustering algorithms a comparative study”, in: Proceedings of 2013 IEEE Conference on Information and Communication Technologies (ICT), pp. 508–513, 2013.
 Bahm, K. Haegler, N.S Maller, C. Plant, CoCo: “coding cost for parameter-free outlier detection”, in: The 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 149–158, June 2009.
 H.P. Kriegel, M. Pfeifle, “Effective and efficient distributed model-based clustering”, in: Proceedings of the 5th International Conference on Data Mining (ICDM’05), pp. 285-265, 2005.
 K.M. Hammouda, M.S. Kamel, “Efficient phrase-based document indexing for web document clustering”, in: IEEE Transactions on Knowledge and Data Engineering, vol. 16(10), pp. 1279–1296, 2004.
 Z. Zhang, J. Zhang, H. Xue, “Improved K-means clustering algorithm”, in: Congress on Image and Signal Processing CISP, vol. 5, pp. 169–172, 2008.
 L. Li, J. You, G. Han, H. Chen, “Double partition around medoids based cluster ensemble”, in: International Conference on Machine Learning and Cybernetics, vol. 4, pp. 1390– 1394, 2012.
 D.H. Zhou, L.Y. Bin, “An improved BIRCH clustering algorithm and application in thermal power”, in: International Conference on Web Information Systems and Mining (WISM), vol. 1, pp. 53–56, 2010.
 R.T. Ng, J. Han, “CLARANS: a method for clustering objects for spatial data mining”, IEEE Transactions on Knowledge and Data Engineering, Vol 14 (5), pp. 1003–1016, 2002.
 M. Ester, H. Kriegel, J. Sander, X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise”, in: Proceeding 2nd International Conference on Knowledge Discovery and Data Mining, , pp. 226–231, 1996.
 H. Shah, K. Napanda, L. D’mello, “ Density Based Algorithms”, in: IJCSE International Journal of Computer Sciences nad Engineering, Vol. 3(11), pp. 54-57, 2015.
 Z. Wang, Y. Hao, Z. Xiong, F. Sun, “SNN clustering kernel technique for content-based scene matching”, in: 7th IEEE International Conference on Cybernetic Intelligent Systems, pp. 1–6, 2008.
 E. Achtert, C. Bohm, H-P. Kriegel, P. Kroger, I. Maller- Gorman, A. Zimek, “Detection and visualization of subspace cluster hierarchies”, in: Advances in Databases: Concepts, Systems and Applications, Lecture Notes in Computer Science, pp. 152–163, 2007.
 D.H. Widyantoro, T.R. Ioerger, J. Yen, “An incremental approach to building a cluster hierarchy”, ICDM Proceedings IEEE International Conference on Data Mining, pp. 705–708, 2002.
 S.A.L. Mary, K.R.S. Kumar, “A density based dynamic data clustering algorithm based on incremental dataset”, J. Computer Sci. Vol 8 (5), pp. 656–664, 2012.
 K.M. Hammouda, M.S. Kamel, “Incremental document clustering using cluster similarity histograms”, in: IEEE/WIC Proceedings International Conference on Web Intelligence, pp. 597–601, 2003.
 S. Young, I. Arel, “A fast and stable incremental clustering algorithm”, in: Seventh International Conference on Information Technology: New Generations (ITNG), pp. 204–209, 2010.