Development and Design of General Data Mining System

DOI

10.2991/itoec-15.2015.26How to use a DOI?

Keywords

Data Mining; Optimal Design; discretization

Abstract

In this paper, we focus on top-down discretization methods and propose a new method for supervised discretization based on class-feature correlation by defining a class-feature contingency factor. The proposed method takes into consideration the distribution of all samples to generate an ideal discretization scheme. The method maintains a high interdependence between the target class and the discretized attribute, and avoids overfitting. Empirical evaluation of seven discretization algorithms on UCI real datasets show that the novel algorithm can yield a better discretization scheme that improves the accuracy of decision tree classification. As to the execution time of discretization and the number of generated rules, our approach also achieves promising results.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Baowen Chen
PY  - 2015/03
DA  - 2015/03
TI  - Development and Design of General Data Mining System
BT  - Proceedings of the 2015 Information Technology and Mechatronics Engineering Conference
PB  - Atlantis Press
SP  - 120
EP  - 123
SN  - 2352-538X
UR  - https://doi.org/10.2991/itoec-15.2015.26
DO  - 10.2991/itoec-15.2015.26
ID  - Chen2015/03
ER  -