Siebes, Arno and Puspitaningrum, Diah (2014) MINING DATABASES TO MINE QUERIES FASTER. In: MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, 7-11 September 2009, Bled, Slovenia.
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Abstract
Inductive databases are databases in which models and patterns are first class citizens. Having models and patterns in the database raises the question: do the models and patterns that are stored help in computing new models and patterns? For example, let C be a classifier on database DB and let Q be a query. Does knowing C speed up the induction of a new classifier on the result of Q? In this paper we answer this problem positively for the code tables induced by our Krimp algorithm. More in particular, assume we have the code tables for all tables in the database. Then we can approximate the code table induced by Krimp on the result of a query, using only these global code tables as candidates. That is, we do not have to mine for frequent item sets on the query result.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Engineering > Department of Informatics Engineering |
| Depositing User: | 021 Nanik Rachmawati |
| Date Deposited: | 27 Mar 2014 04:33 |
| Last Modified: | 27 Mar 2014 04:33 |
| URI: | http://repository.unib.ac.id/id/eprint/6900 |
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