SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Database Systems (TODS)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
A Lazy Approach to Pruning Classification Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Learning rules with negation for text categorization
Proceedings of the 2007 ACM symposium on Applied computing
Classification inductive rule learning with negated features
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
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Associative classification has been recently applied to text document categorization. However, differently from classification of structured data, the quality of the generated classifier is rather low. This effect is mainly due to the poor precision of generated rules.To increase the precision of associative classifiers we propose the use of classification rules including negated words, i.e. words that the considered document should not contain. Rules are in the form "If a document includes words A and B, but not word Z, then it belongs to class C1". Mining classification rules with negated words becomes quickly intractable when decreasing the support threshold. We tackle this problem by means of an opportunistic approach, where negated words are only generated to specialize rules that may wrongly classify training documents. Hence precision is increased, without losing recall.Experiments on the Reuters corpus show that our classifier based on negated words achieves good precision and recall results, while yielding an easily interpretable model typical of associative classifiers.