Mining data streams under block evolution
- 1 January 2002
- journal article
- Published by Association for Computing Machinery (ACM) in ACM SIGKDD Explorations Newsletter
- Vol. 3 (2) , 1-10
- https://doi.org/10.1145/507515.507517
Abstract
In this paper we survey recent work on incremental data mining model maintenance and change detection under block evolution. In block evolution, a dataset is updated periodically through insertions and deletions of blocks of records at a time. We describe two techniques: (1) We describe a generic algorithm for model maintenance that takes any traditional incremental data mining model maintenance algorithm and transforms it into an algorithm that allows restrictions on a temporal subset of the database. (2) We also describe a generic framework for change detection, that quantifies the difference between two datasets in terms of the data mining models they induce.Keywords
This publication has 20 references indexed in Scilit:
- Database technology for decision support systemsComputer, 2001
- Space-efficient online computation of quantile summariesPublished by Association for Computing Machinery (ACM) ,2001
- DEMON: mining and monitoring evolving dataIEEE Transactions on Knowledge and Data Engineering, 2001
- HancockPublished by Association for Computing Machinery (ACM) ,2000
- Mining high-speed data streamsPublished by Association for Computing Machinery (ACM) ,2000
- EddiesPublished by Association for Computing Machinery (ACM) ,2000
- Tracking join and self-join sizes in limited storagePublished by Association for Computing Machinery (ACM) ,1999
- The Space Complexity of Approximating the Frequency MomentsJournal of Computer and System Sciences, 1999
- What makes patterns interesting in knowledge discovery systemsIEEE Transactions on Knowledge and Data Engineering, 1996
- Recent trends in hierarchic document clustering: A critical reviewInformation Processing & Management, 1988