'Max gap' presentation slideshows

Mining Sequential Patterns: Generalizations and Performance Improvements

Mining Sequential Patterns: Generalizations and Performance Improvements. R. Srikant R. Agrawal IBM Almaden Research Center Advisor: Dr. Hsu Presented by: M.H. Lin. Outline. Motivation Objective Introduction Problem Statement The New Algorithm: GSP Performance Evaluation Conclusion

567 views • 29 slides

Mining Sequential Patterns: Generalizations and Performance Improvements

Mining Sequential Patterns: Generalizations and Performance Improvements. R. Srikant R. Agrawal IBM Almaden Research Center Advisor: Dr. Hsu Presented by: M.H. Lin. Outline. Motivation Objective Introduction Problem Statement The New Algorithm: GSP Performance Evaluation Conclusion

304 views • 29 slides

Mining Sequence Data

Mining Sequence Data. Object. Timestamp. Events. A. 10. 2, 3, 5. A. 20. 6, 1. A. 23. 1. B. 11. 4, 5, 6. B. 17. 2. B. 21. 7, 8, 1, 2. B. 28. 1, 6. C. 14. 1, 8, 7. Sequence Data. Sequence Database:. Examples of Sequence Data. Element (Transaction). Event (Item).

580 views • 21 slides

Mining Sequences

214 views • 21 slides

A new algorithm for gap constrained sequence mining

A new algorithm for gap constrained sequence mining. Salvatore Orlando, Raffaele Perego,Claudio Silvestri Proceedings of 2004 ACM Symposium on Applied Computing Advisor ? Jia-Ling Koh

243 views • 18 slides