Title Performance Evaluation of RULES-3 Induction System for Data Mining Author(s) Mehmet Sabih AKSOY, Hassan Mathkour and Bader Ali ALASOOS Contact Info msaksoy@ksu.edu.sa , Telephone # :4677697, Address: King Saud University College of Computer and Information Sciences P.O.Box 51178 RIYADH 11543 Saudi Arabia Department Information Systems

elbowcheepΤεχνίτη Νοημοσύνη και Ρομποτική

15 Οκτ 2013 (πριν από 4 χρόνια και 23 μέρες)

123 εμφανίσεις

Title

Performance Evaluation of RULES
-
3 Induction System for Data Mining

Author(s)

Mehmet Sabih AKSOY, Hassan Mathkour and Bader Ali ALASOOS

Contact Info

msaksoy@ksu.edu.sa ,

Telephone # :4677697,

Address:
King Saud University

College of Computer
and Information Sciences

P.O.Box 51178 RIYADH 11543 Saudi Arabia

Department

Information Systems

Major

Artificial Intelligence

Citation

International Journal of Innovative Computing, Information and Control
(IJICIC),
Vol.6, No.8, Japan.

Year of

Publication

2010

Publisher

IJICIC

Sponsor


Type of

Publication

Journal Paper

ISBN


URL/DOI

http://www.ijicic.org/09
-
0206
-
1.pdf

Full Text

(Yes, No)

Yes

Keywords

Inductive reasoning, Data Mining, Knowledge Acquisition, Rules3, Machine Learning

Abstract

Data mining has been recognized as a key research topic in database systems
and machine learning. It aims to discover a useful knowledge from large amount
of data. Data mining become one of the most important tools used for solving
most of today's

problems that are related to different sectors of our life.
Different techniques have been developed for mining data in statistics, machine
learning, and other disciplines. These techniques need to be re
-
evaluated, and
scalable algorithms should be develo
ped for effective data mining. This paper
will investigate the use of RULES
-
3 Inductive Learning Algorithm for data mining
by comparing it with three statistical, two Lazy, and six rule
-
based data mining
algorithms on eleven real life data sets in terms of

learning rate, accuracy and
robustness to noisy and incomplete data.