ATTENTION: The software behind KU ScholarWorks is being upgraded to a new version. Starting July 15th, users will not be able to log in to the system, add items, nor make any changes until the new version is in place at the end of July. Searching for articles and opening files will continue to work while the system is being updated.
If you have any questions, please contact Marianne Reed at mreed@ku.edu .
Scalable mining for classification rules in relational databases
dc.contributor.author | Wang, Min | |
dc.contributor.author | Iyer, Bala | |
dc.contributor.author | Vitter, Jeffrey Scott | |
dc.date.accessioned | 2011-03-16T15:05:28Z | |
dc.date.available | 2011-03-16T15:05:28Z | |
dc.date.issued | 2004 | |
dc.identifier.citation | M. Wang, B. Iyer, and J. S. Vitter. “Scalable Mining for Classification Rules in Relational Databases,” Herman Rubin Festschrift, Lecture Notes Monograph Series, 45, Institute of Mathematical Statistics, Hayward, CA, Fall 2004. An extended abstract appears in Proceedings of the International Database Engineering & Application Symposium (IDEAS ’98), Cardiff, Wales, July 1998, 58–67. A shorter version appears in Proceedings of the ACM SIGMOD Data Mining and Knowledge Discovery Workshop (DMKD ’98), Seattle, WA, June 1998. http://dx.doi.org/10.1214/lnms/1196285404 | |
dc.identifier.uri | http://hdl.handle.net/1808/7167 | |
dc.description | doi:10.1214/lnms/1196285404 | |
dc.description.abstract | Data mining is a process of discovering useful patterns (knowledge) hidden in extremely large datasets. Classification is a fundamental data mining function, and some other functions can be reduced to it. In this paper we propose a novel classification algorithm (classifier) called MIND (MINing in Databases). MIND can be phrased in such a way that its implementation is very easy using the extended relational calculus SQL, and this in turn allows the classifier to be built into a relational database system directly. MIND is truly scalable with respect to I/O efficiency, which is important since scalability is a key requirement for any data mining algorithm. We have built a prototype of MIND in the relational database management system DB2 and have benchmarked its performance. We describe the working prototype and report the measured performance with respect to the previous method of choice. MIND scales not only with the size of datasets but also with the number of processors on an IBM SP2 computer system. Even on uniprocessors, MIND scales well beyond dataset sizes previously published for classifiers.We also give some insights that may have an impact on the evolution of the extended relational calculus SQL. | |
dc.language.iso | en_US | |
dc.publisher | Institute of Mathematical Statistics | |
dc.title | Scalable mining for classification rules in relational databases | |
dc.type | Article | |
kusw.kuauthor | Vitter, Jeffrey Scott | |
kusw.oastatus | fullparticipation | |
dc.identifier.doi | 10.1214/lnms/1196285404 | |
kusw.oaversion | Scholarly/refereed, publisher version | |
kusw.oapolicy | This item meets KU Open Access policy criteria. | |
dc.rights.accessrights | openAccess |