Lexical analysis and stoplists
Information retrieval
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Improving text retrieval for the routing problem using latent semantic indexing
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Using a generalized instance set for automatic text categorization
Proceedings of the 21st 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)
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical neural networks for text categorization (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Text classification using ESC-based stochastic decision lists
Proceedings of the eighth international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Applying an existing machine learning algorithm to text categorization
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Mining Recurrent Items in Multimedia with Progressive Resolution Refinement
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Machine learning in automated text categorisation
Machine learning in automated text categorisation
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel mining of association rules from text databases
The Journal of Supercomputing
A review of associative classification mining
The Knowledge Engineering Review
Text classification using sentential frequent itemsets
Journal of Computer Science and Technology
Data & Knowledge Engineering
Supervised document classification based upon domain-specific term taxonomies
International Journal of Metadata, Semantics and Ontologies
The Chinese text categorization system with association rule and category priority
Expert Systems with Applications: An International Journal
CCIC: Consistent Common Itemsets Classifier
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Document-Base Extraction for Single-Label Text Classification
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Exploiting temporal contexts in text classification
Proceedings of the 17th ACM conference on Information and knowledge management
An Agent-Oriented Data Mining Framework for Mass Customization in the Automotive Industry
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Efficient generic association rules based classifier approach
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Text clustering using frequent itemsets
Knowledge-Based Systems
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Hybrid DIAAF/RS: statistical textual feature selection for language-independent text classification
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Word co-occurrence features for text classification
Information Systems
GARC: a new associative classification approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Association classification based on sample weighting
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Relevance of counting in data mining tasks
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Term graph model for text classification
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
An improvement of text association classification using rules weights
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Considering re-occurring features in associative classifiers
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On pruning and tuning rules for associative classifiers
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
An associative classifier for uncertain datasets
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Automatic text categorization has always been an important application and research topic since the inception of digital documents. Today, text categorization is a necessity due to the very large amount of text documents that we have to deal with daily. Many techniques and algorithms for automatic text categorization have been devised and proposed in the literature. However, there is still much room for improving the effectiveness of these classifiers, and new models need to be examined. We propose herein a new approach for automatic text categorization. This paper explores the use of association rule mining in building a text categorization system and proposes a new fast algorithm for building a text classifier. Our approach has the advantage of a very fast training phase, and the rules of the classifier generated are easy to understand and manually tuneable. Our investigation leads to conclude that association rule mining is a good and promising strategy for efficient automatic text categorization.