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
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
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
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
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
ACME: an associative classifier based on maximum entropy principle
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
An evolutionary approach to rank class association rules with feedback mechanism
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Building a high accuracy classifier for classification is a problem in real applications. One high accuracy classifier used for this purpose is based on association rules. In the past, some researches showed that classification based on association rules (or class-association rules - CARs) has higher accuracy than that of other rule-based methods such as ILA and C4.5. However, mining CARs consumes more time because it mines a complete rule set. Therefore, improving the execution time for mining CARs is one of the main problems with this method that needs to be solved. In this paper, we propose a new method for mining class-association rule. Firstly, we design a tree structure for the storage frequent itemsets of datasets. Some theorems for pruning nodes and computing information in the tree are developed after that, and then, based on the theorems, we propose an efficient algorithm for mining CARs. Experimental results show that our approach is more efficient than those used previously.