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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Survey Paper on Quality Cluster Generation Using Random Projections

P.A. Gat, K.S.Kadam, , ,
Affiliations
Department of Computer Science, D.K.T.E. Society’s Textile and Engineering Institute, Ichalkaranji, India
:10.22362/ijcert/2018/v5/i12/v5i1203


Abstract
Clustering is the grouping of a particular set of objects based on their characteristics, aggregating them according to their similarities. Regarding data mining, this methodology partitions the data implementing a specific join algorithm, most suitable for the desired information analysis. Clusters will obtained by using density-based clustering and DBSCAN clustering. DBSCAN cluster is a fast clustering technique, large complexity and requires more parameters. To overcome these problems uses the OPTICS Density-based algorithm. The algorithm requires single factor, namely the least amount of points in a cluster which can necessary as input in density- based technique. Using random projection improving the cluster quality and runtime.


Citation
. International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.5, Issue 12, December - 2018,


Keywords : Cluster Analysis, Random Projections, Neighbouring.

References
[1] Ester M, Krigel H-P, Sander J, Xu X(1996)”A Density-based algorithm for discovering clusters in large spatial databases either noise.” In proceeding of the ACM conference knowledge discovery and data mining (KDD), pp 226-231.
[2] Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) “Optics: ordering points to identify the clustering structure” In: Proceedings of the ACM international conference on management of data (SIGMOD), pp. 49–60.       
[3] Alexander Hinneburg, Daniel A. Keim (1998),"An Efficient Approach to Clustering in Large Multimedia Databases with Noise [Online] Available: http://www.aaai.org.
[4] Hinneburg A, Gabriel H-H (2007) Denclue 2.0: fast clustering based on kernel density estimation. In Advances in intelligent data analysis (IDA), pp 70–80.
[5] Imran Khan, Joshua Zhexue Huang (2012),” Ensemble Clustering of High Dimensional Data With random Projection.” In: Proceeding of the international conference on information and knowledge management.
[6] Schneider J, Vlachos M (2013) “Fast parameter less density-based clustering via random projections.” In: Proceedings of the international conference on information and knowledge management (CIKM), pp 861–866.
[7] Johannes Schneider, Michail Valchos(2017) “Scalable Density-based clustering with quality guarantees using random projections.” Published in Journal: Data Mining and Knowledge Discovery Volume 31 Issue 4, July 2017 pages 972-1005.


DOI Link : https://doi.org/10.22362/ijcert/2018/v5/i12/v5i1203

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