Integrating classification capability and reliability in associative classification: A β-stronger model

Authors:
Yuanchun Jiang;Yezheng Liu;Xiao Liu;Shanlin Yang
Affiliations:
School of Management, Hefei University of Technology, Hefei, Anhui 230009, China and The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA;School of Management, Hefei University of Technology, Hefei, Anhui 230009, China and Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Anhui 230 ...;Faculty of information and Communication Technologies, Swinburne University of Technology, Melbourne 3122, Australia;School of Management, Hefei University of Technology, Hefei, Anhui 230009, China and Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Anhui 230 ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 10
Cited 1

Variable precision rough set model

Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An associative classification-based recommendation system for personalization in B2C e-commerce applications

Expert Systems with Applications: An International Journal
A new approach to classification based on association rule mining

Decision Support Systems
MCAR: multi-class classification based on association rule

AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A review of associative classification mining

The Knowledge Engineering Review
Mining the data from a hyperheuristic approach using associative classification

Expert Systems with Applications: An International Journal
CSMC: A combination strategy for multi-class classification based on multiple association rules

Knowledge-Based Systems

An approach for adaptive associative classification

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Mining class association rules is an important task for associative classification and plays a key role in rule-based decision support systems. Most of the existing methods try the best to mine rules with high reliability but ignore their capability for classifying potential objects. This paper defines a concept of @b-stronger relationship, and proposes a new method that integrates classification capability and classification reliability in rule discovery. The method takes advantage of rough classification method to generate frequent items and rules, and calculate their support and confidence degrees. We propose two new theorems to prune redundant frequent items and a concept of indiscernibility relationship between rules to prune redundant rules. The pruning theorems afford the associative classifier with good classification capability. The experiment shows that the proposed method generates a smaller frequent item set and significantly enhances the classification performance.