TCS: a shell for content-based text categorization
Proceedings of the sixth conference on Artificial intelligence applications
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Automated learning of decision rules 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
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Rule-based word clustering for text classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Hybrid-patent classification based on patent-network analysis
Journal of the American Society for Information Science and Technology
Hi-index | 12.05 |
This paper proposes an innovative pattern-oriented associative rule-based approach to construct automatic TRIZ-based patent classification system. Derived from associative rule-based text categorization, the new approach does not only discover the semantic relationship among features in a document by their co-occurrence, but also captures the syntactic information by manually generalized patterns. We choose 7 classes which address 20 of the 40 TRIZ Principles and perform experiments upon the binary set for each class. Compared with three currently popular classification algorithms (SVM, C4.5 and NB), the new approach shows some improvement. More importantly, this new approach has its own advantages, which were discussed in this paper as well.