Aggregation of Association Rules
| dc.contributor.author | Shichao ZHANG | en_US |
| dc.contributor.author | Xindong WU | en_US |
| dc.date.accessioned | 2004-10-21T14:28:52Z | en_US |
| dc.date.accessioned | 2017-01-23T06:59:46Z | |
| dc.date.available | 2004-10-21T14:28:52Z | en_US |
| dc.date.available | 2017-01-23T06:59:46Z | |
| dc.date.issued | 1999-07-01T00:00:00Z | en_US |
| dc.description.abstract | Dealing with very large databases is one of the defining challenges in data mining research and development. Some databases are simply too large (e.g., with terabytes of data) to be processed at one time, for efficiency and space reasons, so splitting them into subsets for processing is a necessary step. Also, some organizations have different data sources (e.g., different branches of a large company), and while putting all data from different sources might amass a huge database for centralized processing, mining rules at different data sources and forwarding the rules (rather than the original raw data) to the centralized company headquarter provides a feasible way to deal with very large database problems. This paper presents a model of aggregating association rules from different data sources. Each data source could also be a subset of a very large database, and so the aggregation model is applicable to both dealing with very large databases by splitting them into subsets, and processing data from different data sources. | en_US |
| dc.format.extent | 267518 bytes | en_US |
| dc.format.extent | 1412874 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.format.mimetype | application/postscript | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/1402 | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TRA7/99 | en_US |
| dc.title | Aggregation of Association Rules | en_US |
| dc.type | Technical Report | en_US |
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