SQL-Like Language for Database Mining.
Tadeusz Morzy, Maciej Zakrzewicz:
SQL-Like Language for Database Mining.
ADBIS 1997: 311-317@inproceedings{DBLP:conf/adbis/MorzyZ97,
author = {Tadeusz Morzy and
Maciej Zakrzewicz},
title = {SQL-Like Language for Database Mining},
booktitle = {Proceedings of the First East-European Symposium on Advances
in Databases and Information Systems (ADBIS'97), St.-Petersburg,
September 2-5, 1997. Volume 1: Regular Papers},
publisher = {Nevsky Dialect},
year = {1997},
pages = {311-317},
ee = {db/conf/adbis/MorzyZ97.html},
crossref = {DBLP:conf/adbis/97},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Data mining, also referred to as database mining or knowledge discovery
in databases (KDD), is a new research area that aims at the discovery of
useful information from large datasets. One of the most interesting and
important research problems is discovering of different types of rules
(e.g. association, characteristic, discriminant, etc.) from data.
In this work we propose the new SQL-like language for data mining in
relational databases, called MineSQL, developed within the scope of the
data mining research project led in Poznan University of Technology.
MineSQL is the extension of industry standard SQL language developed for
expressing rule queries and assisting a user in rule generation, storage and
retrieval. We focus on the main features of the language, its syntax and
semantics, illustrated by practical examples.
Copyright © 1997 by the ACM,
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BibTeX
References
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A Database Perspective on Knowledge Discovery.
Commun. ACM 39(11): 58-64(1996) BibTeX
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SIGMOD Conference 1993: 207-216 BibTeX
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A New SQL-like Operator for Mining Association Rules.
VLDB 1996: 122-133 BibTeX
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- ...
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CIKM 1994: 401-407 BibTeX
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Mining Generalized Association Rules.
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Sampling Large Databases for Association Rules.
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Referenced by
- Marek Wojciechowski:
Mining Various Patterns in Sequential Data in an SQL-like Manner.
ADBIS (Short Papers) 1999: 131-138
- Marek Wojciechowski, Maciej Zakrzewicz:
Itemset Materializing for Fast Mining of Association Rules.
ADBIS 1998: 284-295
BibTeX
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