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Performance Analysis for Visual Data Mining Classification Techniques of Decision Tree, Ensemble and SOM

by C. M. Velu, Kishana R. Kashwan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 57 - Number 22
Year of Publication: 2012
Authors: C. M. Velu, Kishana R. Kashwan
10.5120/9426-3874

C. M. Velu, Kishana R. Kashwan . Performance Analysis for Visual Data Mining Classification Techniques of Decision Tree, Ensemble and SOM. International Journal of Computer Applications. 57, 22 ( November 2012), 57-63. DOI=10.5120/9426-3874

@article{ 10.5120/9426-3874,
author = { C. M. Velu, Kishana R. Kashwan },
title = { Performance Analysis for Visual Data Mining Classification Techniques of Decision Tree, Ensemble and SOM },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 57 },
number = { 22 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 57-63 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume57/number22/9426-3874/ },
doi = { 10.5120/9426-3874 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:01:10.529287+05:30
%A C. M. Velu
%A Kishana R. Kashwan
%T Performance Analysis for Visual Data Mining Classification Techniques of Decision Tree, Ensemble and SOM
%J International Journal of Computer Applications
%@ 0975-8887
%V 57
%N 22
%P 57-63
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research paper is a comprehensive report on experimental setup, data collection methods, implementation and result analyses of market segmentation and forecasting using neural network based artificial intelligence tools. The main focus of the paper is on visual data mining applications for enhancing business decisions. The software based system is implemented as a fully automated and intelligent enough to take into effect of each sales transaction. It updates and instantly modifies forecasting statistics by receiving input sales data directly from sales counter through networked connectivity. The connectivity may be wired or wireless. Three artificial intelligence tools, namely decision tree, ensemble classifier and Self Organizing Maps (SOM) are used for data processing and data analysis. The visual data mining concept is implemented by presenting results in the form of visual interpretation in as simple as possible way to understand very complex statistics. The current research results are mapped to interactive visualization by using multi-level pie charts, multi bar charts, histograms, scatter plots, tree maps and dataflow diagrams. The different visualization techniques help in understanding different levels of information hidden in very large data sets. The results analysis show that decision tree has classified data correctly up to a 86. 0 %, ensemble techniques produced an average of 88. 0 % and the predictions using SOM has accuracy of 90. 0 %. The survey carried out after implementation and use of the system shows that the system is very easy to understand and can be interpreted quickly with minimum efforts.

References
  1. Jiawei Han and Micheline Kamber, 2008, Data Mining Concepts and Techniques, Elsevier, Second Edition.
  2. Luka Furst, Sanja Fidler, Ales Leonardis, 2008, Selection Features for Object Detection Using an AdaBoost-Compatible Evaluation Function, Pattern Recognition Letters, Vol. 29, pp. 1603-1612.
  3. Muzammil Khan and Sarwar Shah Khan, 2011, Data and Information Visualization Methods and Interactive Mechanisms: A Survey, International Journal of Computer Applications, 34 (1), pp. 1-13.
  4. North C. , 2005, Towards Measuring Visualization Insight, IEEE Computer Graphics and Applications, 11(4), pp. 443-456
  5. Alfredo R. Teyseyre and Marcelo R. Campo, 2009, An Overview of 3D Software Visualization, IEEE Transactions on Visualization and Computer Graphics, 15 (1), pp. 87-105.
  6. C. Johnson, 2004, Top Scientific Visualization Research Problems, IEEE Computer Graphics and Applications, 24(4), pp. 13-17.
  7. J. J. Thomas and K. A. Cook, 2006, A Visual Analytics Agenda, IEEE Transactions on Computer Graphics and Applications, 26 (1), pp. 12-19.
  8. Huirong Zhang Yun Chen, 2009, An Analysis of the Applications of Data Mining in Airline Company CRM, Fuzzy Systems and Knowledge Discovery, Sixth IEEE International Conference on, Vol 7, pp. 290 – 293.
  9. Huaping Gong Qiong Xia, 2009, Study on Application of Customer Segmentation Based on Data Mining Technology, Future Computer and Communication, FCC, IEEE International Conference on, pp. 167 – 170.
  10. Wu Dong Sheng, 2011, Application Study on Banks's CRM Based on Data Mining Technology, Electrical Information and Control Engineering, ICEICE, IEEE International Conference on, pp. 5727 – 5731.
  11. Young Sung Cho Keun Ho Ryu, 2008, Implementation of Personalized Recommendation System Using Demographic Data and RFM Method in e-commerce, Management of Innovation and Technology, ICMIT, 4TH IEEE International Conference on, pp. 475 – 479.
  12. Lim Chia Yean and Khoo, V. K. T. , 2010, Customer relationship management: Computer-assisted Tools for Customer Lifetime Value Prediction, Information Technology, ITSim, IEEE International Symposium, pp. 1180 – 1185.
  13. Shaw M. J. , Subramaniam C. , Tan G. W. and Welge M. E. , 2001, Knowledge management and Data Mining for marketing, Decision Support Systems, pp. 127 - 137.
  14. Song H. S. , Kim J. K. and Kim S. H. , 2001, Mining the Change of Customer Behavior in an Internet Shopping Mall, Expert Systems with Applications, 21(3), pp. 157 - 170.
  15. Jill Dyche, 2002, The CRM Handbook: A Business Guide to CRM, Addison-Wesley Professional, First Edition.
  16. A. Berson, K. Thearling and S. Smith, 2000, Building DM Applications for CRM, McGraw-Hill.
  17. Alex Sheshunoff, 1999, Winning CRM Strategies, ABA Banking Journal, pp. 54 - 66.
  18. Thomas G. Dietterich, 2000, Ensemble Methods in Machine Learning, ACM Proceedings of the First International Workshop on Multiple Classifier Systems, pp. 1-15.
  19. Rui Xia, Chengqing Zong, Shoushan Li, 2011, Ensemble of feature sets and classification algorithms for sentiment classification, Information Sciences, 181, pp. 1138-1152.
  20. B. H. Chandra Sekhar and G. Sobha, 2009, Classification of documents using Kohonen's self-organizing map, International Journal of Computer Theory and Engineering, 1(5), pp. 610-613
Index Terms

Computer Science
Information Sciences

Keywords

Visualization techniques visual data mining neural networks decision tree forecasting business decision

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