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
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.
. International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.5, Issue 12, December - 2018,
Keywords : Cluster Analysis, Random Projections, Neighbouring.
 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.
 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.
 Alexander Hinneburg, Daniel A. Keim (1998),"An Efficient Approach to Clustering in Large Multimedia Databases with Noise [Online] Available: http://www.aaai.org.
 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.
 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.
 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.
 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.
We have kept IJCERT is a free peer-reviewed scientific journal to endorse conservation. We have not put up a paywall to readers, and we do not charge for publishing. But running a monthly journal costs is a lot. While we do have some associates, we still need support to keep the journal flourishing. If our readers help fund it, our future will be more secure.