OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Pattern-growth methods for frequent pattern mining
Pattern-growth methods for frequent pattern mining
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
An experimental study on large-scale web categorization
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A sampling-based framework for parallel data mining
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
IEEE Transactions on Knowledge and Data Engineering
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Multiple labels associative classification
Knowledge and Information Systems
Multi-label Associative Classification of Medical Documents from MEDLINE
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Considering re-occurring features in associative classifiers
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Entropy-based associative classification algorithm for mining manufacturing data
International Journal of Computer Integrated Manufacturing
Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Multi-label Classification with Gene Expression Programming
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A niching algorithm to learn discriminant functions with multi-label patterns
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Efficient mining of jumping emerging patterns with occurrence counts for classification
Transactions on rough sets XIII
PISA: A framework for multiagent classification using argumentation
Data & Knowledge Engineering
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Associative-classification is a promising classification method based on association-rule mining. Significant amount of work has already been dedicated to the process of building a classifier based on association rules. However, relatively small amount of research has been performed in association-rule mining from multi-label data. In such data each example can belong, and thus should be classified, to more than one class. This paper aims at the most demanding, with respect to computational cost, part in associative-classification, which is efficient generation of association rules. This task can be achieved using different frequent pattern mining methods. In this paper, we propose a new method that is based on the state-of-the-art tree-projection-based frequent pattern mining algorithm. This algorithm is modified to improve its efficiency and extended to accommodate the multi-label recurrent-item associative-classification rule generation. The proposed algorithm is tested and compared with A priori-based associative-classification rule generator on two large datasets.