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, Presented by Xxxxxx

Outline  Motivation  Methodology  Feature Extraction  Clustering and Categorizing  Data Mining VS Text Mining  Conclusion

Motivation  Problem: Most of data in a company is unstructured or semi-structured  Examples:  Letters  s  Phone transcripts  Contracts

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

Methodology  Aspect 1: Knowledge Discovery  Aspect 2: Information Distillation Approaches: Extraction Analysis

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

Clustering Model

Categorization Model

Data Mining VS Text Mining Data MiningText Mining GoalDiscover hidden modelsDiscover hidden facts MethodTries to generalize all of data into a single model Tries to understand the details, cross reference between individual instances FieldsMarketing, medicine, health care Biosciences, customer profile analysis

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