SQL-Like Language for Database Mining.

Tadeusz Morzy, Maciej Zakrzewicz: SQL-Like Language for Database Mining. ADBIS 1997: 311-317
  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,}


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.

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ACM SIGMOD Anthology

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Tomasz Imielinski, Heikki Mannila: A Database Perspective on Knowledge Discovery. Commun. ACM 39(11): 58-64(1996) BibTeX
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Database Mining: A Performance Perspective. IEEE Trans. Knowl. Data Eng. 5(6): 914-925(1993) BibTeX
Rakesh Agrawal, Tomasz Imielinski, Arun N. Swami: Mining Association Rules between Sets of Items in Large Databases. SIGMOD Conference 1993: 207-216 BibTeX
Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499 BibTeX
Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM 39(11): 27-34(1996) BibTeX
Rosa Meo, Giuseppe Psaila, Stefano Ceri: A New SQL-like Operator for Mining Association Rules. VLDB 1996: 122-133 BibTeX
Jiawei Han, Yongjian Fu: Discovery of Multiple-Level Association Rules from Large Databases. VLDB 1995: 420-431 BibTeX
Jiawei Han, Yongjian Fu, Wei Wang, Jenny Chiang, Wan Gong, Krzysztof Koperski, Deyi Li, Yijun Lu, Amynmohamed Rajan, Nebojsa Stefanovic, Betty Xia, Osmar R. Zaïane: DBMiner: A System for Mining Knowledge in Large Relational Databases. KDD 1996: 250-255 BibTeX
Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo: Finding Interesting Rules from Large Sets of Discovered Association Rules. CIKM 1994: 401-407 BibTeX
Ashok Savasere, Edward Omiecinski, Shamkant B. Navathe: An Efficient Algorithm for Mining Association Rules in Large Databases. VLDB 1995: 432-444 BibTeX
Ramakrishnan Srikant, Rakesh Agrawal: Mining Generalized Association Rules. VLDB 1995: 407-419 BibTeX
Hannu Toivonen: Sampling Large Databases for Association Rules. VLDB 1996: 134-145 BibTeX

Referenced by

  1. Marek Wojciechowski: Mining Various Patterns in Sequential Data in an SQL-like Manner. ADBIS (Short Papers) 1999: 131-138
  2. Marek Wojciechowski, Maciej Zakrzewicz: Itemset Materializing for Fast Mining of Association Rules. ADBIS 1998: 284-295
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