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Analysis of Prediction Techniques based on Classification and Regression

by Pinki Sagar, Prinima, Indu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 7
Year of Publication: 2017
Authors: Pinki Sagar, Prinima, Indu
10.5120/ijca2017913623

Pinki Sagar, Prinima, Indu . Analysis of Prediction Techniques based on Classification and Regression. International Journal of Computer Applications. 163, 7 ( Apr 2017), 47-51. DOI=10.5120/ijca2017913623

@article{ 10.5120/ijca2017913623,
author = { Pinki Sagar, Prinima, Indu },
title = { Analysis of Prediction Techniques based on Classification and Regression },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 7 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 47-51 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number7/27409-2017913623/ },
doi = { 10.5120/ijca2017913623 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:34.059799+05:30
%A Pinki Sagar
%A Prinima
%A Indu
%T Analysis of Prediction Techniques based on Classification and Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 7
%P 47-51
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – making it more accurate, reliable, efficient and beneficial. In data mining various techniques are used- classification, clustering, regression, association mining. These techniques can be used on various types of data; it may be stream data, one dimensional, two dimensional or multi-dimensional data. In this paper we analyze the data mining techniques based on various parameters. All data mining techniques used in various fields for prediction and extraction of useful data or knowledge from a large data base is analyzed and each data mining technique has different performance.

References
  1. L. Breiman, J.H. Friedman, R.A. Olshen, and C.T. Stone. Classification and regression Trees. Wadsworth, Belmont, California, 1984.
  2. C.L. Blake D.J. Newman, S. Hettich and C.J. Merz. UCI repository of machine learning databases, 1998.
  3. T. Elomaa and M. K ̈a ̈ari ̈ainen. An analysis of reduced error pruning.Journal of ArticialntelligenceResearch , 15:163–187, 2001.
  4. Usama M. Fayyad. Data mining and knowledge discovery: Makin g senseout of data. IEEE Expert: Intelligent Systems and Their Applications
  5. 11(5):20–25, 1996. A. Feelders. classification trees. ttp://www.cs.uu.nl/docs/vakken/adm/trees.pdf.
  6. R. Kruse G. Della Riccia and H. Lenz.Computational Intelligence inData Mining. Springer, New York, NY, USA, 2000.
  7. N. Landwehr, M. Hall, and E. Frank. Logistic model trees, 2003
  8. J. Ross Quinlan.C4.5: programs for machine learning. Morgan Kauf-mann Publishers Inc., San Francisco, CA, USA, 1993.
  9. Ian H. Witten and EibeFrank.Data Mining: Practical machine learningtools and techniques. Morgan Kaufmann Publishers Inc., San francisco,CA, USA, 2nd edition, 2005.
  10. D.F. Andrews, :A robust method for multiple linear regression,Technometrics, vol16,1974,pp125–127
  11. Chai, EunHeeKim and Long Jin:predictionof Frequent Items to OneDimensionalStream Data; Fifth International Conference on Computational Science and Applications; page353-360,2001
  12. Y. Chen, G.Dong, J.Han, B.W.Wah, andJ.Wang:.Multi dimensionalRegressionAnalysisofTime-Series DataStreams; Proc.Int.Conf.Very LargeDataBases;HongKong,China, Aug.2002.
  13. R. Hayward; A Basic Approach to Linear Regression; RWJ linical Scholars Program; pp1-3,University of Michigan , 2005.
  14. O.B.Yaik, C.H.Yong, and FHaron, Time Series Prediction using Adaptive Association rules,InProc.of DFMA05, pp.310-314, 2005.
  15. Omid Rouhani-Kalleh; Algorithms for Fast Large Scale data Mining Using Logistic Regression; Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining; pp 155-162,2007
  16. Feng Zhao, Qing-Hua A Li :A Plane Regression Based Sequence Forecast Algorithms for Stream Data ; Proc. of the Fourth International Conference on Machine Learning and Cybernetics; pp-1559-1562 Guangzhou,18-21August, 2005.
  17. Y. Peng, G. Kou, Y. Shi, Z. Chen; A Descriptive Framework for the Field of Data Mining and Knowledge Discovery. International Journal of Information Technology and Decision Making, Volume 7, Issue 4: 639 – 682; 2000
  18. Perlich, C,Provost, F., Simonoff, J. S. TreeInduction verses. Logistic Regression:A Learning-Curve
  19. Analysis. Journal of Machine Learning Research Vol. 4 pp-211- 255. 2003.
  20. Amir Bar-Or, Daniel Keren, Assaf Schuster, and Ran Wolff: Hierarchical Decision Tree Induction in istributed Genomic Databases; IEEERANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,VOL. 17;pp;1138- 1150,2007.
  21. Qi Luo; Advancing Knowledge Discovery and Data Mining; Workshop on Knowledge Discovery and Data Mining pp;3-5, 2008.
  22. Fayyad, Usama; Gregory Piatetsky-Shapiro, and adhraic Smyth; From Data Mining to Knowledge Discovery in Databases. -pp:12-17, June 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Classification Prediction Clustering Association

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