C4.5: programs for machine learning
C4.5: programs for machine learning
Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Textual data mining of service center call records
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule discovery from textual data based on key phrase patterns
Proceedings of the 2004 ACM symposium on Applied computing
An e-mail analysis method based on text mining techniques
Applied Soft Computing
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A text mining system uses two kinds of background knowledge: a concept relation dictionary and a key concept dictionary. The concept relation dictionary consists of a set of rules. We can automatically acquire it by using an inductive learning algorithm. The algorithm uses training examples including concepts that are generated by using both lexical analysis and the key concept dictionary. The algorithm cannot deal with a training example with more than one concept in the same attribute. Such a training example is apt to generate from a report, when the concept dictionary is not well defined. It is necessary to extend an inductive learning algorithm, because the dictionary is usually not completed. This paper proposes an inductive learning method that deals with the report. Also, the paper shows the efficiency of the method through some numerical experiments using business reports about retailing.