Open AccessProceedings Article
DataMine : application programming interface and query language for database mining
Tomasz Imielinski,Aashu Virmani,Amin Abdulghani +2 more
- 02 Aug 1996
- pp 256-261
TL;DR: The goal of the DataMine and the work is to make the next step in the development of DBMS and provide much needed support for the rule discovery applications.
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Abstract: The main objective of the DataMine is to provide application development interface to develop knowledge discovery applications on the top of large databases. Current database systems have been designed mainly to support business applications. The success of SQL capitalized on a small number of primitives which are sufficient to support a vast majority of applications today. Unfortunately this is not enough to capture the emerging family of new applications dealing with the so called rule and knowledge discovery. The goal of the DataMine and our work is to make the next step in the development of DBMS and provide much needed support for the rule discovery applications.
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Citations
Data Mining: Concepts and Techniques (2nd edition)
Jiawei Han,Micheline Kamber +1 more
- 01 Jan 2006
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Data mining: an overview from a database perspective
TL;DR: In this paper, a survey of the available data mining techniques is provided and a comparative study of such techniques is presented, based on a database researcher's point-of-view.
The Role of Occam‘s Razor in Knowledge Discovery
TL;DR: It is argued that Occam's razor's continued use in KDD risks causing significant opportunities to be missed, and should therefore be restricted to the comparatively few applications where it is appropriate.
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Visually Aided Exploration of Interesting Association Rules
Bing Liu,Wynne Hsu,Ke Wang,Shu Chen +3 more
- 26 Apr 1999
TL;DR: This paper proposes a new framework to allow the user to explore the discovered rules to identify those interesting ones, and has two components, an interestingness analysis component, and a visualization component.
Modeling KDD Processes within the Inductive Database Framework
Jean-François Boulicaut,Mika Klemettinen,Heikki Mannila +2 more
- 01 Sep 1999
TL;DR: This work formalizes this concept and shows how it can be used throughout the whole process of data mining due to the closure property of the framework, and shows that simple query languages can be defined using normal database terminology.
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Rakesh Agrawal,Tomasz Imielinski,Arun N. Swami +2 more
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TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
•Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 12 Sep 1994
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
•Proceedings Article
Fast algorithms for mining association rules
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 01 Jul 1998
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Database mining: a performance perspective
TL;DR: The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented and an algorithm for classification obtained by combining the basic rule discovery operations is given.
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