Munich Personal RePEc Archive
Login | Create Account

Data Mining Decision Trees in Economy

Badulescu, Laviniu-Aurelian and Nicula, Adrian (2007): Data Mining Decision Trees in Economy. Published in: Analele Universitatii din Oradea, Stiinte Economice , Vol. II, No. XVI (2007): pp. 723-727.

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
345Kb

Abstract

Data Mining represents the extraction previously unknown, and potentially useful information from data. Using Data Mining Decision Trees techniques our investigation tries to illustrate how to extract meaningful socio-economical knowledge from large data sets. Our tests find 5 attributes selection measures that perform more accurate then the best performance of the 17 algorithms presented in literature.

Item Type:MPRA Paper
Language:English
Keywords:Data Mining, Decision Trees, classification error rate
Subjects:C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology; Computer Programs
ID Code:9579
Deposited By:Laviniu-Aurelian Badulescu
Deposited On:16. Jul 2008 02:46
Last Modified:16. Jul 2008 02:46
References:

Alves A., Camacho R., Oliveira E., “Inductive Logic Programming for Data Mining in Economics”, in Proc. of the 2nd International Workshop on Data Mining and Adaptive Modeling Methods for Economics and Management, Pisa, September 2004.

Borgelt C., “A decision tree plug-in for DataEngine”, in Proc. European Congress on Intelligent Techniques and Soft Computing (EUFIT), vol. 2, 1998, pp. 1299-1303.

Jessen H.C., Paliouras G., “Data Mining in Economics, Finance, and Marketing”, in Lecture Notes in Computer Science, Vol. 2049/2001, Springer Berlin/Heidelberg, 2001, p. 295.

Kohavi R., “Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid”, in Proc. of the 2nd International Conf. on Knowledge Discovery and Data Mining, 1996, pp. 202-207.

Kudyba S.(ed.), “Managing Data Mining, Advice from Experts”, IT Solutions Series, Idea Group, USA, 2004, pp. VII-VIII.

Larose D.T., “Data Mining Methods and Models”, John Wiley & Sons, Hoboken, New Jersey, 2006, pp. 18-25.

Lazăr A., “Knowledge Discovery for Large Data Sets”, Youngstown State University, 2003, http://www.cis.ysu.edu/~alazar/pdf/2003ResearchProposal.pdf.

Nayak R., “Data Mining and Mobile Business Data”, in Khosrow-Pour M.(ed.) Encyclopedia of information science and technology, vol. II, Idea Group, 2005, p. 700.

Payne A., “Handbook of CRM: Achieving Excellence in Customer Management”, Elsevier Butterworth-Heinemann, Great Britain, 2005, p. 67.

Peng J., Du P., “Classification with Different Models on Adult Income”, 2002, http://citeseer.ist.psu.edu/cache/papers/cs/27570/http:zSzzSzwww.cas.mcmaster.cazSz~cs4tf3zSzprojectzSzreport_he.pdf/classification-with-different-models.pdf

Siciliano R., Conversano C., “Decision tree induction”, in Wang, J.(ed.), Encyclopedia of data warehousing and mining, Idea Group, USA, 2006, p. 353.

Thomasian A., “Active disks for Data Mining”, in Wang, J.(ed.) Encyclopedia of Data Warehousing and Mining, Idea Group, USA, 2006, p. 6.

Witten I.H., Frank E., “Data mining: practical machine learning tools and techniques”, 2nd ed., Elsevier, Morgan Kaufmann, USA, 2005, p. 5.

All papers reproduced by permission. Reproduction and distribution subject to the approval of the copyright owners.
Repository Staff Only: item control page

LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.