Mining Classification Rules from Datasets with Large Number of Many-Valued Attributes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Improving an Association Rule Based Classifier
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Boosting an Associative Classifier
IEEE Transactions on Knowledge and Data Engineering
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
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 Novel Rule Ordering Approach in Classification Association Rule Mining
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Multi-label Lazy Associative Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
Blind paraunitary equalization
Signal Processing
The Pre-FUFP algorithm for incremental mining
Expert Systems with Applications: An International Journal
Maintenance of fast updated frequent pattern trees for record deletion
Computational Statistics & Data Analysis
An efficient and effective association-rule maintenance algorithm for record modification
Expert Systems with Applications: An International Journal
Autonomous classifiers with understandable rule using multi-objective genetic algorithms
Expert Systems with Applications: An International Journal
Building a Rule-Based Classifier—A Fuzzy-Rough Set Approach
IEEE Transactions on Knowledge and Data Engineering
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
An efficient algorithm for incremental mining of temporal association rules
Data & Knowledge Engineering
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Mining minimal non-redundant association rules using frequent itemsets lattice
International Journal of Intelligent Systems Technologies and Applications
Building a highly-compact and accurate associative classifier
Applied Intelligence
An incremental mining algorithm for maintaining sequential patterns using pre-large sequences
Expert Systems with Applications: An International Journal
Calibrated lazy associative classification
Information Sciences: an International Journal
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
ACME: an associative classifier based on maximum entropy principle
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Analysis of association rule mining on quantitative concept lattice
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
International Journal of Intelligent Information and Database Systems
MEI: An efficient algorithm for mining erasable itemsets
Engineering Applications of Artificial Intelligence
| Hi-index | 12.05 |
Classification plays an important role in decision support systems. A lot of methods for mining classification rules have been developed in recent years, such as C4.5 and ILA. These methods are, however, based on heuristics and greedy approaches to generate rule sets that are either too general or too overfitting for a given dataset. They thus often yield high error ratios. Recently, a new method for classification from data mining, called the Classification Based on Associations (CBA), has been proposed for mining class-association rules (CARs). This method has more advantages than the heuristic and greedy methods in that the former could easily remove noise, and the accuracy is thus higher. It can additionally generate a rule set that is more complete than C4.5 and ILA. One of the weaknesses of mining CARs is that it consumes more time than C4.5 and ILA because it has to check its generated rule with the set of the other rules. We thus propose an efficient pruning approach to build a classifier quickly. Firstly, we design a lattice structure and propose an algorithm for fast mining CARs using this lattice. Secondly, we develop some theorems and propose an algorithm for pruning redundant rules quickly based on these theorems. Experimental results also show that the proposed approach is more efficient than those used previously.