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Text mining : Finding nuggets in mountains of textual data

Author : Jochen Dijrre , Peter Gerstl , Roland Seiffert Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , San Diego, California, August 15-18, 1999, 398-401. Presented by Xxxxxx. Text mining : Finding nuggets in mountains of textual data.

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Text mining : Finding nuggets in mountains of textual data

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  1. Author : JochenDijrre, Peter Gerstl, Roland Seiffert Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, August 15-18, 1999, 398-401. Presented by Xxxxxx Text mining : Finding nuggets in mountains of textual data

  2. Outline • Motivation • Methodology • Feature Extraction • Clustering and Categorizing • Data Mining VS Text Mining • Conclusion

  3. Motivation • Problem: Most of data in a company is unstructured or semi-structured • Examples: • Letters • Emails • Phone transcripts • Contracts

  4. Definition and Application • Text mining: The discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. • Applications: • Summarizing documents • Discovering/monitoring relations among people • Customer profile analysis • Trend analysis • Documents summarization

  5. Methodology • Aspect 1: Knowledge Discovery • Aspect 2: Information Distillation Approaches: • Extraction • Analysis

  6. Feature Extraction • Recognize and classify significant vocabulary items from the text • Categories of vocabulary • Proper names • Multiword terms • Abbreviations • Relations • Other useful things

  7. Clustering Model

  8. Categorization Model

  9. Data Mining VS Text Mining

  10. Conclusion • Introduction of text mining • Differences between data mining and text mining • Overview of IBM’s Intelligent Miner for Text • The tools and methods used in the past

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