Interestingness Measures for Association Rules: What Do They Really Measure?

Interestingness Measures for Association Rules: What Do They Really Measure?

Yun Sing Koh (Auckland University of Technology, New Zealand), Richard O’Keefe (University of Otago, New Zealand) and Nathan Rountree (University of Otago, New Zealand)
Copyright: © 2008 |Pages: 23
DOI: 10.4018/978-1-59904-960-1.ch002
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Association rules are patterns that offer useful information on dependencies that exist between the sets of items. Current association rule mining techniques such as apriori often extract a very large number of rules. To make sense of these rules we need to order or group the rules in some fashion such that the useful patterns are highlighted. The study of this process involves the investigation of an “interestingness” in the rules. To date, various measures have been proposed but unfortunately, these measures present inconsistent information about the interestingness of a rule. In this chapter, we show that different metrics try to capture different dependencies among variables. Each measure has its own selection bias that justifies the rationale for preferring it compared to other measures. We present an experimental study of the behaviour of the interestingness measures such as lift, rule interest, Laplace, and information gain. Our experimental results verify that many of these measures are very similar in nature. From the findings, we introduce a classification of the current interestingness measures.

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Table of Contents
David Taniar
Chapter 1
Riadh Ben Messaoud, Sabine Loudcher Rabaséda, Rokia Missaoui, Omar Boussaid
Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting... Sample PDF
OLEMAR: An Online Environment for Mining Association Rules in Multidimensional Data
Chapter 2
Yun Sing Koh, Richard O’Keefe, Nathan Rountree
Association rules are patterns that offer useful information on dependencies that exist between the sets of items. Current association rule mining... Sample PDF
Interestingness Measures for Association Rules: What Do They Really Measure?
Chapter 3
Qin Ding, Gnanasekaran Sundarraj
With the growing usage of XML in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi-structured data... Sample PDF
Mining Association Rules from XML Data
Chapter 4
Yue-Shi Lee, Show-Jane Yen
Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Web... Sample PDF
A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns
Chapter 5
Tushar, Tushar, Shibendu Shekhar Roy, Dilip Kumar Pratihar
Clustering is a potential tool of data mining. A clustering method analyzes the pattern of a data set and groups the data into several clusters... Sample PDF
Determination of Optimal Clusters Using a Genetic Algorithm
Chapter 6
ABM Shawkat Ali
Clustering technique in data mining has received a significant amount of attention from machine learning community in the last few years as one of... Sample PDF
K-means Clustering Adopting rbf-Kernel
Chapter 7
Pradeep Kumar, P. Radha Krishna, Raju S. Bapi, T. M. Padmaja
In recent years, advanced information systems have enabled collection of increasingly large amounts of data that are sequential in nature. To... Sample PDF
Advances in Classification of Sequence Data
Chapter 8
Justin Zhan
To conduct data mining, we often need to collect data from various parties. Privacy concerns may prevent the parties from directly sharing the data... Sample PDF
Using Cryptography For Privacy-Preserving Data Mining
Chapter 9
Domain Driven Data Mining  (pages 196-223)
Longbing Cao, Chengqi Zhang
Quantitative intelligence based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications.... Sample PDF
Domain Driven Data Mining
Chapter 10
Model Free Data Mining  (pages 224-252)
Can Yang, Jun Meng, Shanan Zhu, Mingwei Dai
Input selection is a crucial step for nonlinear regression modeling problem, which contributes to build an interpretable model with less... Sample PDF
Model Free Data Mining
Chapter 11
John Wang, Xiaohua Hu, Dan Zhu
This research explores the effectiveness of data mining in a commercial perspective. Statistical issues are specified first. Data accuracy and... Sample PDF
Minimizing the Minus Sides of Mining Data
Chapter 12
Tu Bao Ho, Thanh Phuong Nguyen, Tuan Nam Tran
The objective of this paper is twofold. First is to provide a survey of computational methods for protein-protein interaction (PPI) study. Second is... Sample PDF
Study of Protein-Protein Interactions from Multiple Data Sources
Chapter 13
Anthony Scime, Gregg R. Murray, Wan Huang, Carol Brownstein-Evans
Immense public resources are expended to collect large stores of social data, but often these data are under-examined thereby missing potential... Sample PDF
Data Mining in the Social Sciences and Iterative Attribute Elimination
Chapter 14
Marco A. Alvarez, SeungJin Lim
Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The... Sample PDF
A Machine Learning Approach for One-Stop Learning
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