Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Temporal document retrieval model for business news archives
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
A practical text summarizer by paragraph extraction for Thai
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Using names and topics for new event detection
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Topic tracking based on bilingual comparable corpora and semisupervised clustering
ACM Transactions on Asian Language Information Processing (TALIP)
Multilingual news clustering: Feature translation vs. identification of cognate named entities
Pattern Recognition Letters
Multidocument Summary Generation: Using Informative and Event Words
ACM Transactions on Asian Language Information Processing (TALIP)
Discovering relationships among categories using misclassification information
Proceedings of the 2008 ACM symposium on Applied computing
Storyline-based summarization for news topic retrospection
Decision Support Systems
Quality Evaluation for Document Relation Discovery Using Citation Information
IEICE - Transactions on Information and Systems
An experiment with association rules and classification: post-bagging and conviction
DS'05 Proceedings of the 8th international conference on Discovery Science
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
visualRSS: a platform to mine and visualise social data from RSS feeds
ICWE'12 Proceedings of the 12th international conference on Current Trends in Web Engineering
| Hi-index | 0.00 |
Association among news articles is useful information for us to track situation related to events, persons, organizations and other concerned issues as well as to detect inconsistency among news. In this paper, we propose an association-based approach towards mining relations in Thai news articles by exploiting coincident terms. This approach first transforms news documents into term-document representation, applies term weighting techniques and generates association by means of statistics. In the work, either unigram or bigram is used for term representation, term frequency, boolean frequency and their modification with inverse document frequency are alternatively applied for term weighting, and confidence or conviction is in turn selected for association measure. Due to this combination, sixteen possible methods are investigated using approximately 811 Thai news of three categories, i.e., politics, economics, and crime. The ranked relations obtained by each method are compared with evaluation done by human. As the result, the method using bigram, term frequency, and conviction achieves the best performance with a rank-order mismatch of 0.84% on the top-50 mined relations. For the top-300 mined relations, the method with bigram, term frequency with inverse document frequency and conviction performs the best with 6.98% rank-order mismatch.