CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach

by P. Anbalagan, R.M. Chandrasekaran
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 124 - Number 2
Year of Publication: 2015
Authors: P. Anbalagan, R.M. Chandrasekaran
10.5120/ijca2015905346

P. Anbalagan, R.M. Chandrasekaran . A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach. International Journal of Computer Applications. 124, 2 ( August 2015), 5-9. DOI=10.5120/ijca2015905346

@article{ 10.5120/ijca2015905346,
author = { P. Anbalagan, R.M. Chandrasekaran },
title = { A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 124 },
number = { 2 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume124/number2/22074-2015905346/ },
doi = { 10.5120/ijca2015905346 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:19.309173+05:30
%A P. Anbalagan
%A R.M. Chandrasekaran
%T A Parallel Weighted Decision Tree Classifier for Complex Spatial Landslide Analysis: Big Data Computation Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 124
%N 2
%P 5-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective and efficient strategies to acquire manage and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era, brings up challenges to traditional data mining algorithms. The processing capacity, architecture and algorithms of traditional database system are not coping with big data analysis. Big Data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this paper is to propose Parallel Weighted Decision Tree Classifier to handle complex spatial landslide big data using Map Reduce programming model. The Proposed Classifier performance is validated with massive dataset. The results indicate that our classifier exhibits both time efficiency and scalability.

References
  1. H. I. Witten and E. Frank, “Data Mining: Practical machine learning tools and techniques”, Morgan Kaufmann, 2005.
  2. M. J. Berry and G. S. Linoff, “Data mining techniques: For marketing, sales, and customer support”, John Wiley & Sons, Inc., 1997.
  3. J.R. Quinlan, “Decision trees and decision-making”, IEE Transactions Systems, Man and Cybernetics, Vol. 20, No.2, pp. 336-346, 1990.
  4. J. R. Quinlan, “C4.5: programs for machine learning”, Morgan Kaufmann, 1993.
  5. J. R. Quinlan, “Improved use of continuous attributes in C4.5”, arXiv preprint cs/9603103, 1996.
  6. J. R. Quinlan, “Induction of decision trees”, Machine Learning, vol. 1, no. 1, pp. 81-106,1986.
  7. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I.Stoica and M. Zaharia, “A view of cloud computing”, Communications of the ACM, vol. 53, no. 4, pp.50-58, 2010.
  8. D. Howe, M. Costanzo, P. Fey, T. Gojobori, L. Hannick, W. Hide, D.P. Hill, R. Kania, M. Schaeffer, S.S.Pierre, S. Twigger, O. White and S.Y. Rhee. “Big data: The future of biocuration”, Nature, vol.455, no.72 09, pp.47-50, 2008.
  9. P. Zikopoulos and C. Eaton, “Understanding big data: Analytics for enterprise class hadoop and streaming data”, McGraw-Hill Osborne Media, 2011.
  10. V. Kumar, A. Grama, A. Gupta and G. Karypis, “Introduction to parallel computing”Redwood City: Benjamin/Cummings, vol. 110, 1994.
  11. K. W. Bowyer, L. O. Hall, T. Moore, N. Chawla and W. P. Kegelmeyer, “A parallel decision tree builder for mining very large visualization datasets”, IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 1888-1893, 2000.
  12. J. Shafer, R. Agrawal and M. Mehta, “SPRINT: A scalable parallel classifier for data mining”, Proc. 1996 Int.Conf. Very Large Data Bases, 1996.
  13. F.Berzal, J.C.Cubero, F.Cuenca, and M.J.Martín-Bautista, “On the quest for easy-to-understand splitting rules”, Data and Knowledge Engineering, Vol. 44, No. 1, pp. 31–48, 2003.
  14. J.L.Polo, F.Berzal, and J.C.Cubero, “Taking class importance into account”, Lecture Notes in Computer Science, 2007.
  15. Xindong Wu,Xingquan Zhu,Gong-Qing Wu,and WeiDing.S, “Data Mining with BigData”, IEEE transactions on knowledge and data engineering, Vol.26, No.1, january2014.
  16. Venkatesan M, Rajawat A S, Arunkumar T, Anbarasi M, Malarvizhi K, “GIS Based Data Mining Classification Approaches for Landslide Susceptibility Analysis”, International Journal of Applied Environmental Sciences, Volume 9, Number 5, pp. 2345-2357, 2014.
  17. Venkatesan.M, Arunkumar .Thangavelu, and Prabhavathy.P, “An Improved Bayesian Classification Data mining Method for Early Warning Landslide Susceptibility Model Using GIS”, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications 9BICTA, Advances in Intelligent Systems and Computing Springer India 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Big Data Classifier Spatial Data Map Reduce Landslide..

Powered by PhDFocusTM