Decision Tree Technology in Data Classification

Hong Yan Zhao

doi:10.4028/www.scientific.net/AMM.268-270.1752

Paper Titles

A Granular Computing Based Decision Tree Algorithm and its Application in Intrusion Detection
p.1730

Estimating Weights of Evaluation Factors on Basis of Monte Carlo Simulation
p.1735

Sales Impacts of Location Based Advertising Using Wireless Communication Technology
p.1741

Based on Texture Feature of Color Image Retrieval
p.1748

Decision Tree Technology in Data Classification
p.1752

Customer Demands Data Acquire Algorithm Research
p.1758

Attacks to Li et al.’s Billing Service User Authentication Scheme
p.1762

The Strategic Interaction on Endogenous Infinite Lattice and the Simulation
p.1768

Steam Turbine Remote Maintenance Service Support Technology Based on RFID
p.1774

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 268-270Decision Tree Technology in Data Classification

Decision Tree Technology in Data Classification

Abstract:

With the development of database technology as well as the widespread application of database management system, the capability of collecting data was improved rapidly, and lots of data have been accumulated. Data mining was created for the purpose of excavate the useful knowledge hidden behind these data. Data classification is not only an important issue of data mining but also an effective KDD method. Decision Tree, which is a major technology of data classification, is applied far and widely. In this article, the concrete step of mining data by decision tree, the main algorithm and the basic idea of decision tree were summarized and analysed.

You might also be interested in these eBooks

Materials, Mechanical Engineering and Manufacture

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 268-270)

Pages:

1752-1757

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.268-270.1752

Citation:

Cite this paper

Online since:

December 2012

Authors:

Hong Yan Zhao

Keywords:

Data Mining (DM), Decision Tree

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Ming Fan, Xiaofeng Meng,the concept and technology of data mining,Beijing, mechanical industry press, (2000)

Google Scholar

[2] Mehmed Kantardzic,Siqing Shan,Data Mining Concepts, Models, Methods and Algorithms,Bei Jing,Tsinghua University Press,(2003)

Google Scholar

[3] [Jingnong Weng,data mining tutorial, Beijing, tsinghua university press, (2003)

Google Scholar

[4] Yingzhuang Zhu，A two stage decision tree construction method and its application,Computer Engineering,(2004)

Google Scholar

[5] J.R. Quinlan, Induction of Decision Trees,Machine Learning,1986,1:81-106

Google Scholar

[6] J.R. Quinlan,C4.5:Programs for Machine Learning, San Mateo, Calif: Morgan Kaufmann,(1993)

Google Scholar

[7] Wanli Zhou, Hongze Qiu, Zuohai Yin: genetic algorithm based on multi attribute fuzzy decision tree optimization,computational engineering and applications, 2009,45(3)

Google Scholar

[8] Janikow C Z.Fuzzy decision trees：issues and methods [J].IEEE Transaction on Systems，Man, and Cybernetics，Part B:Cybernetics,1998,28(1):1-14

DOI: 10.1109/3477.658573

Google Scholar

[9] Chang Sun,Qi Song, the event based on the discrete degree of the decision tree construction method，control and decision, 2008,1

Google Scholar

[10] Xu Chang, Yijie Li, Wanjun Liu, CDC and REP combination of pruning decision tree optimization algorithm,Computer Engineering, 2012,7

Google Scholar