NDLI: On the adaptability of G3PARM to the extraction of rare association rules

Content Provider	Springer Nature Link
Author	Luna, J. M. Romero, J. R. Ventura, S.
Copyright Year	2013
Abstract	To date, association rule mining has mainly focused on the discovery of frequent patterns. Nevertheless, it is often interesting to focus on those that do not frequently occur. Existing algorithms for mining this kind of infrequent patterns are mainly based on exhaustive search methods and can be applied only over categorical domains. In a previous work, the use of grammar-guided genetic programming for the discovery of frequent association rules was introduced, showing that this proposal was competitive in terms of scalability, expressiveness, flexibility and the ability to restrict the search space. The goal of this work is to demonstrate that this proposal is also appropriate for the discovery of rare association rules. This approach allows one to obtain solutions within specified time limits and does not require large amounts of memory, as current algorithms do. It also provides mechanisms to discard noise from the rare association rule set by applying four different and specific fitness functions, which are compared and studied in depth. Finally, this approach is compared with other existing algorithms for mining rare association rules, and an analysis of the mined rules is performed. As a result, this approach mines rare rules in a homogeneous and low execution time. The experimental study shows that this proposal obtains a small and accurate set of rules close to the size specified by the data miner.
Starting Page	391
Ending Page	418
Page Count	28
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	38
Issue Number	2
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2013-02-09
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rare association rules Grammar-guided genetic programming Evolutionary computation Information Systems and Communication Service Business Information Systems
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

Mining exceptional relationships with grammar-guided genetic programming

G3PARM: A Grammar Guided Genetic Programming algorithm for mining association rules

Improving a multi-objective evolutionary algorithm to discover quantitative association rules

An evolutionary algorithm for the discovery of rare class association rules in learning management systems

Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Exploration of Ordinal Data Using Association Rules

Constraining and summarizing association rules in medical data

WAR: Weighted association rules for item intensities

On the adaptability of G3PARM to the extraction of rare association rules

Similar Documents

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

Mining exceptional relationships with grammar-guided genetic programming

G3PARM: A Grammar Guided Genetic Programming algorithm for mining association rules

Improving a multi-objective evolutionary algorithm to discover quantitative association rules

An evolutionary algorithm for the discovery of rare class association rules in learning management systems

Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Exploration of Ordinal Data Using Association Rules

Constraining and summarizing association rules in medical data

WAR: Weighted association rules for item intensities

On the adaptability of G3PARM to the extraction of rare association rules