Representation and learning in information retrieval
Representation and learning in information retrieval
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An example-based mapping method for text categorization and retrieval
ACM Transactions on Information Systems (TOIS)
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Feature Subset Selection in Text-Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International 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
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Optimization of Association Word Knowledge Base through Genetic Algorithm
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
EC-WEB '02 Proceedings of the Third International Conference on E-Commerce and Web Technologies
Automatic classification for grouping designs in fashion design recommendation agent system
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Personalization method for tourist point of interest (POI) recommendation
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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In this paper, we propose effective feature selection method using association word mining. Documents are represented as association-word-vectors that include a few words instead of single words. The focus in this paper is the association rule in reduction of a high dimensional feature space. The accuracy and recall of document classification depend on the number of words for composing association words, confidence, and support at Apriori algorithm. We show how confidence, support, and the number of words for composing association words at Apriori algorithm are selected efficiently. We have used Naive Bayes classifier on text data using proposed feature-vector document representation. By experiment for categorizing documents, we have proved that feature selection method of association word mining is more efficient than information gain and document frequency.