Selection criteria for text mining approaches |
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| Affiliation: | 1. The American College of Greece;2. College of Computer and Information Sciences;3. Instituto Politécnico Nacional;4. Department of Applied Informatics;5. University of Science and Technology of China (USTC);6. King Abdulaziz University;1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, ?o?nierska 49, 71-410 Szczecin, Poland;2. Institute of Informatics, Wroc?aw University of Technology, Wroc?aw, Poland;3. Knowledge Media Research Center, Tübingen, Germany;1. College of Computers and Information Technology, Taif University, Taif, Saudi Arabia;2. University College, Zayed University, United Arab Emirates;3. College of Computer and Information Systems, Umm al Qura University, Saudi Arabia |
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| Abstract: | Text mining techniques include categorization of text, summarization, topic detection, concept extraction, search and retrieval, document clustering, etc. Each of these techniques can be used in finding some non-trivial information from a collection of documents. Text mining can also be employed to detect a document’s main topic/theme which is useful in creating taxonomy from the document collection. Areas of applications for text mining include publishing, media, telecommunications, marketing, research, healthcare, medicine, etc. Text mining has also been applied on many applications on the World Wide Web for developing recommendation systems. We propose here a set of criteria to evaluate the effectiveness of text mining techniques in an attempt to facilitate the selection of appropriate technique. |
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| Keywords: | Text mining approaches Classification Clustering Selection criteria |
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