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Selection and aggregation of interestingness measures: a review

Association Rule Mining is the process of retrieving frequent patterns that occur in a transaction database. Initially used as a market basket analysis solution for retail businesses, it has grown to cover many other fields such as medicine [1, 2], traffic estimation [3] and anomaly detection [4, 5]...

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Main Authors: Anwar, Toni, Bong, Kok Keong, Christoph, Quix, Machnizam, Selvakumar, Matthias, Joest
Format: Article
Published: Asian Research Publishing Network (ARPN) 2014
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/62544/
http://www.jatit.org/volumes/Vol59No1/17Vol59No1.pdf
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spelling my.utm.625442017-06-18T06:11:03Z http://eprints.utm.my/id/eprint/62544/ Selection and aggregation of interestingness measures: a review Anwar, Toni Bong, Kok Keong Christoph, Quix Machnizam, Selvakumar Matthias, Joest QA75 Electronic computers. Computer science Association Rule Mining is the process of retrieving frequent patterns that occur in a transaction database. Initially used as a market basket analysis solution for retail businesses, it has grown to cover many other fields such as medicine [1, 2], traffic estimation [3] and anomaly detection [4, 5]. An association rule has two components (antecedent and consequent) which is derived from a pattern (a set of items). However, it is not clear when investigating a frequent item set, which items imply the others (i.e., which is antecedent, and which is consequent). Therefore, several combinations of items as antecedent and consequent are generated. This leads to a huge amount of association rules being output by an algorithm for Association Rule Mining. Thus, it is imperative that data miners require some type of measures to evaluate the "interestingness" of these rules. There exist in excess of 70 well-known measures and countless other manually crafted measures in the literature. In this survey, we systematically discuss the methods which users could use to select or aggregate the interestingness measures, applicability of such methods and evaluation of the usage of such methods. Asian Research Publishing Network (ARPN) 2014 Article PeerReviewed Anwar, Toni and Bong, Kok Keong and Christoph, Quix and Machnizam, Selvakumar and Matthias, Joest (2014) Selection and aggregation of interestingness measures: a review. Journal of Theoretical and Applied Information Technology, 59 (1). pp. 146-166. ISSN 1992-8645 http://www.jatit.org/volumes/Vol59No1/17Vol59No1.pdf
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Anwar, Toni
Bong, Kok Keong
Christoph, Quix
Machnizam, Selvakumar
Matthias, Joest
Selection and aggregation of interestingness measures: a review
description Association Rule Mining is the process of retrieving frequent patterns that occur in a transaction database. Initially used as a market basket analysis solution for retail businesses, it has grown to cover many other fields such as medicine [1, 2], traffic estimation [3] and anomaly detection [4, 5]. An association rule has two components (antecedent and consequent) which is derived from a pattern (a set of items). However, it is not clear when investigating a frequent item set, which items imply the others (i.e., which is antecedent, and which is consequent). Therefore, several combinations of items as antecedent and consequent are generated. This leads to a huge amount of association rules being output by an algorithm for Association Rule Mining. Thus, it is imperative that data miners require some type of measures to evaluate the "interestingness" of these rules. There exist in excess of 70 well-known measures and countless other manually crafted measures in the literature. In this survey, we systematically discuss the methods which users could use to select or aggregate the interestingness measures, applicability of such methods and evaluation of the usage of such methods.
format Article
author Anwar, Toni
Bong, Kok Keong
Christoph, Quix
Machnizam, Selvakumar
Matthias, Joest
author_facet Anwar, Toni
Bong, Kok Keong
Christoph, Quix
Machnizam, Selvakumar
Matthias, Joest
author_sort Anwar, Toni
title Selection and aggregation of interestingness measures: a review
title_short Selection and aggregation of interestingness measures: a review
title_full Selection and aggregation of interestingness measures: a review
title_fullStr Selection and aggregation of interestingness measures: a review
title_full_unstemmed Selection and aggregation of interestingness measures: a review
title_sort selection and aggregation of interestingness measures: a review
publisher Asian Research Publishing Network (ARPN)
publishDate 2014
url http://eprints.utm.my/id/eprint/62544/
http://www.jatit.org/volumes/Vol59No1/17Vol59No1.pdf
_version_ 1643655450818772992
score 13.211869

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