A Review: Design and Development of Novel Techniques for Clustering and Classification of Data

R.S. Walse¹ , G.D. Kurundkar² , P. U. Bhalchandra³

Section:Review Paper, Product Type: Journal Paper
Vol.06 , Special Issue.01 , pp.19-22, Jan-2018

Online published on Jan 31, 2018

Copyright © R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra . 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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Abstract :
Data Distribution can be obtained by clustering data. In this work we observed the characteristics of selected cluster, and make a further study on particular clusters. Also, cluster analysis generally acts as the preprocessing of other data mining operations. Consequently, cluster analysis has become a very active research topic in data mining. Data mining is a new technology, developing with database as well as artificial intelligence. It is a processing procedure of extracting credible and effective novel techniques and understandable patterns from the database. Cluster analysis can be important data mining method used to figure out the data segmentation and pattern information. The development of data mining methods, different types of clustering techniques establish. The study of clustering method from the perception of statistics, based on the statistical theory, The review of this paper make an effort to combine statistical method with the machine learning algorithm technique as well as introduce the existing best r-statistical softwares, including factor, correspondence and analysis of functional data into data mining. The present study is undertaken to develop a Data Mining workflow using clustering and classification of data, solving clustering problem as well as extracting association rules. Use the suitable proximity measure in addition to that to select the optimal clustering model to solve clustering problems. Develop a Data Mining workflow to extract association rules.

Key-Words / Index Term :
ISODATA, SRIDHCR, r-solution, K-means

References :
[1] A perusal of big data classification and Hadoop technology, International Transaction of electrical and computer engineers system, 2017, Vol 4, No.1, 26-38.
[2] http://www.cs.put.poznan.pl/jstefanowski/sed/DM-7clusteringnew.pdf
[3] http://www.ijarcsms.com/docs/paper/volume2/issue12/V2I12-0095.pdf
[4] M. and Heckerman, D., “An experimental comparison of several clustering and initialization method,” Technical Report MSR TR-98-06, Microsoft
[5] Research, Redmond, WA, February 1998.
[6] Mrs. Bharati M. Ramageri, “Data Mining Techniques and Applications,” Indian Journal of Computer Science and Engineering, Vol. 1 No. 4, pp.301-305, 2010.
[7] Karimella Vikram and Niraj Upadhyaya, “Data Mining Tools and Techniques: a review,” Computer Engineering and Intelligent Systems, Vol 2, No.8.Pp.31-39, 2011.
[8] Usama Fayyad, G. Paitetsky-Shapiro, and Padhrais Smith, “knowledge discovery and data mining: Towards a unifying framework,” proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 82-22, 1996
[9] A Novel Method for Text and Non-Text Segmentation in Document Images International conference on Communication and Signal Processing, April 3-5, 2013, India,
[10] Novel Techniques on feature clustering algorithms for text classification IJCST Vol 4, Issue Spl-4 Oct-Dec, 2013. ISSN: 2229-4333 (Print)
[11] A Novel Method for text and Non-Text segmentation in document images. International Conference on Communication and signal processing, April 3-5, 2013 978-1-4673-4866-9/13 2013 IEEE.