An introduction to genetic algorithms
An introduction to genetic algorithms
Data mining: concepts and techniques
Data mining: concepts and techniques
Evaluating the novelty of text-mined rules using lexical knowledge
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Syntagmatic and paradigmatic representations of term variation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Sentence Similarity Based on Semantic Nets and Corpus Statistics
IEEE Transactions on Knowledge and Data Engineering
Evolving new lexical association measures using genetic programming
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Artificial intelligence and intelligent systems research in Chile
Artificial intelligence
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Text mining discovers unseen patterns in textual databases. But these discoveries are useless unless they contribute valuable knowledge for users who make strategic decisions. Confronting this issue can lead to a complicated activity called knowledge discovery from texts, which deals with both discovering unseen knowledge and evaluating this potentially valuable knowledge. KDT can benefit from techniques that have been useful in data mining or knowledge discovery from databases. However, we can't immediately apply data mining techniques to text data for text mining because they assume a structure in the source data that isn't in free text. We must therefore use new representations for text data. An evolutionary approach that combines information extraction technology and genetic algorithms can produce a new, integrated hypothesis for text mining.