Survey Paper on Clustering of High Dimensional Data Streams

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-50 Number-1
Year of Publication : 2017
Authors : C Kondaiah, Dr.P.Chandra Sekhar

MLA

C Kondaiah, Dr.P.Chandra Sekhar "Survey Paper on Clustering of High Dimensional Data Streams". International Journal of Computer Trends and Technology (IJCTT) V50(1):63-67, August 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The data stream problem has been studied extensively in recent years because thecollection of streaming data is very easy. So Clustering of streaming data is essential for classification and decision making. Yet, a lot of stream data is high dimensional in nature. Finding clustering in high dimensional data is a difficult task because of high dimensional data comprises hundreds of attributes.Density-based clustering algorithms treat clusters as the dense regions it’s useful for the clustering of High dimensional data than conventional algorithms. Propose a new, high dimensional, projected data stream clustering method, called HPStream method. The method is implementing by combining a fading cluster structure, and the projection based clustering methodology.

References
[1]. SunitaJahirabadkar, Parag Kulkarni., ?Clustering for High Dimensional Data: Density based Subspace Clustering Algorithms, International Journal of Computer Applications (0975 – 8887) Volume 63– No.20, February 2013.
[2]. Michael Hahsler, MatthewBola˜nos., ?Clustering Data Streams Based on Shared Density Between Micro-Clusters, IEEE Transactions On Knowledge And Data Engineering — Preprint, Accepted 1/17/2016.
[3]. Lance Parsons, EhteshamHaque, Huan Liu., ?Subspace Clustering for High Dimensional Data: A Review,ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets: Volume 6 Issue 1, June 2004.
[4].Chairukwattana R., Kangkachit T., Rakthanmanon T., Waiyamai K., ?Evolution-Based Clustering of High Dimensional Data Streams with Dimension Projection, Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 245. Springer, 2014.
[5]. Feng Cao, Martin Ester,WeiningQian,Aoying Zhou., ?Density-Based Clustering over an Evolving Data Stream with Noise, SIAM International Conference on Data Mining,2006.
[6].LeventErtöz, Michael Steinbach, Vipin Kumar., ?A New Shared Nearest Neighbor Clustering Algorithm and its Applications.,Workshop on Clustering High Dimensional Data and its Applications at 2nd SIAM International Conference on Data Mining,(2002)
[7] S. Guha, N. Mishra, R. Motwani, and L. O’Callaghan, ?Clustering data streams, in Proc. ACM Symp. Found. Comput. Sci., 12–14 Nov. 2000, pp. 359–366.
[8] C. Aggarwal, Data Streams: Models and Algorithms, (series Advances in Database Systems). New York, NY, USA: Springer-Verlag, 2007.
[9] J. Gama, Knowledge Discovery from Data Streams, 1st Ed. London, U.K.: Chapman & Hall, 2010.
[10] Y. Chen and L. Tu, ?Density-based clustering for real-time stream data, in Proc. 13th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2007, pp. 133–142.
[11] L. Wan, W. K. Ng, X. H. Dang, P. S. Yu, and K. Zhang, ?Density-based clustering of data streams at multiple resolutions, ACM Trans. Knowl. Discovery from Data, vol. 3, no. 3, pp. 1–28, 2009.
[12] AminehAmini, Teh Ying Wah., ?Density Micro-Clustering Algorithms on Data Streams: A Review?, Proceedings of the international multiconference of Engineers and scientists 2011, vol1,IMESC, March 16-18-2011,Hong Kong [13].http://en.wikipedia.org/wiki/Eigenface.
[14]. http://shoefer.github.io/intuitivemi/2015/07/19/data-numbers-representations.html.

Keywords
Clustering Data Streams, High Dimensional Data, projected clustering, High Dimensional Data Mining.