Multidimensional Model-Based Decision Rules Mining

Multidimensional Model-Based Decision Rules Mining

Qinrong Feng (Tongji University, China), Duoqian Miao (Tongji University, China) and Ruizhi Wang (Tongji University, China)
DOI: 10.4018/978-1-60566-404-0.ch016
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Abstract

Decision rules mining is an important technique in machine learning and data mining, it has been studied intensively during the past few years. However, most existing algorithms are based on flat data tables, from which sets of decision rules mined may be very large for massive data sets. Such sets of rules are not easily understandable and really useful for users. Moreover, too many rules may lead to over-fitting. Thus, a method of decision rules mining from different abstract levels was provided in this chapter, which aims to improve the efficiency of decision rules mining by combining the hierarchical structure of multidimensional model and the techniques of rough set theory. Our algorithm for decision rules mining follows the so called separate-and-conquer strategy. Namely, certain rules were mined beginning from the most abstract level, and supporting sets of those certain rules were removed from the universe, then drill down to the next level to recursively mine other certain rules which supporting sets are included in the remaining objects until no objects remain in the universe or getting to the primitive level. So this algorithm can output some generalized rules with different degree of generalization.
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Introduction

Decision rules mining is an important technique in data mining and machine learning. It has been widely used in business, medicine and finance, etc. A multitude of promising algorithms of decision rules mining (Xiaohua Hu & Nick Cercone, 1997, 2001; Michiyuki Hirokane, et al. 2007; María C. Fernández, et al. 2001) have been developed during the past few years.

The aim of decision rules mining is to find a good rule set, which has a minimum number of rules and each rule should be as short as possible. We all know, each row of a decision table specifies a decision rule that determine decisions in terms of conditions. Thus, a set of decision rules mined from a decision table may be very large for a massive data set. Such a set of rules are not easily understandable and really useful for users. Moreover, too many rules may lead to over-fitting. Most existing methods to this problem follow the strategy of post-processing for those mined decision rules.

Marek Sikora and Marcin Michalak (2006) summed up that post-processing of decision rules mining can be implemented in two ways: rules generalization (rules shortening and rules joining) and rules filtering (rules that are not needed in view of certain criterion are removed from the final rules set). The technique of rules generalization is to obtain rules from detailed level first, and then use the techniques of generalization for these rules, combination or joining to reduce the number of decision rules. The technique of rules filtering is accomplished by template, interestingness or constraint to further process those rules mined from detailed level.

Most existing algorithms of decision rules mining only manipulate data at individual level, namely, they have not consider the case of multiple abstract levels for the given data set. However, in many applications, data contain structured information which is multidimensional and multilevel in nature, such as e-commerce, stocks, scientific data, etc. That is, there exists partial order relation among some attribute values in the given dataset, such as day, month, quarter and year for Time attribute. The partial order relation among attribute values for each attribute can be represented by a tree or a lattice, which constitutes a concept hierarchy. Obviously, there exists the relation of generalization or specification among concepts at different levels.

The technique of rules generalization is to obtain rules from detailed level first, and then use the techniques of generalization for these rules, combination or joining to reduce the number of decision rules. While in this chapter, we first generalize the given data set according to concept hierarchies built in advance to form a multidimensional data cube, then mine decision rules at various levels of abstraction. This not only can obtain generalized rules, but also can mine decision rules from different levels of abstraction. Of course, the algorithm in this chapter can reduce the number of mined rules greatly.

In this chapter, an approach to mine decision rules from various abstract levels is provided, which aims to improve the efficiency of decision rules mining by combining the multidimensional model (Antoaneta Ivanova & Boris Rachev, 2004) and rough set theory (Z. Pawlak, 1991; Wang Guoyin, 2001; Zhang Wenxiu et al. 2003).

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Table of Contents
Foreword
David Bell
Acknowledgment
Yanchang Zhao, Chengqi Zhang, Longbing Cao
Chapter 1
Paul D. McNicholas, Yanchang Zhao
Association rules present one of the most versatile techniques for the analysis of binary data, with applications in areas as diverse as retail... Sample PDF
Association Rules: An Overview
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Chapter 2
Mirko Boettcher, Georg Ruß, Detlef Nauck, Rudolf Kruse
Association rule mining typically produces large numbers of rules, thereby creating a second-order data mining problem: which of the generated rules... Sample PDF
From Change Mining to Relevance Feedback: A Unified View on Assessing Rule Interestingness
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Chapter 3
Solange Oliveira Rezende, Edson Augusto Melanda, Magaly Lika Fujimoto, Roberta Akemi Sinoara, Veronica Oliveira de Carvalho
Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that... Sample PDF
Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules
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Chapter 4
Julien Blanchard, Fabrice Guillet, Pascale Kuntz
Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous... Sample PDF
Semantics-Based Classification of Rule Interestingness Measures
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Chapter 5
Huawen Liu, Jigui Sun, Huijie Zhang
In data mining, rule management is getting more and more important. Usually, a large number of rules will be induced from large databases in many... Sample PDF
Post-Processing for Rule Reduction Using Closed Set
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Chapter 6
Hacène Cherfi, Amedeo Napoli, Yannick Toussaint
A text mining process using association rules generates a very large number of rules. According to experts of the domain, most of these rules... Sample PDF
A Conformity Measure Using Background Knowledge for Association Rules: Application to Text Mining
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Chapter 7
Hetal Thakkar, Barzan Mozafari, Carlo Zaniolo
The real-time (or just-on-time) requirement associated with online association rule mining implies the need to expedite the analysis and validation... Sample PDF
Continuous Post-Mining of Association Rules in a Data Stream Management System
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Chapter 8
Ronaldo Cristiano Prati
Receiver Operating Characteristics (ROC) graph is a popular way of assessing the performance of classification rules. However, as such graphs are... Sample PDF
QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules
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Chapter 9
Maria-Luiza Antonie, David Chodos, Osmar Zaïane
The chapter introduces the associative classifier, a classification model based on association rules, and describes the three phases of the model... Sample PDF
Variations on Associative Classifiers and Classification Results Analyses
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Chapter 10
Silvia Chiusano, Paolo Garza
In this chapter the authors make a comparative study of five well-known classification rule pruning methods with the aim of understanding their... Sample PDF
Selection of High Quality Rules in Associative Classification
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Chapter 11
Sadok Ben Yahia, Olivier Couturier, Tarek Hamrouni, Engelbert Mephu Nguifo
Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules.... Sample PDF
Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules
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Chapter 12
Claudio Haruo Yamamoto, Maria Cristina Ferreira de Oliveira, Solange Oliveira Rezende
Miners face many challenges when dealing with association rule mining tasks, such as defining proper parameters for the algorithm, handling sets of... Sample PDF
Visualization to Assist the Generation and Exploration of Association Rules
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Chapter 13
Nicolas Pasquier
After more than one decade of researches on association rule mining, efficient and scalable techniques for the discovery of relevant association... Sample PDF
Frequent Closed Itemsets Based Condensed Representations for Association Rules
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Chapter 14
Mengling Feng, Jinyan Li, Guozhu Dong, Limsoon Wong
This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the... Sample PDF
Maintenance of Frequent Patterns: A Survey
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Chapter 15
Guozhu Dong, Jinyan Li, Guimei Liu, Limsoon Wong
This chapter considers the problem of “conditional contrast pattern mining.” It is related to contrast mining, where one considers the mining of... Sample PDF
Mining Conditional Contrast Patterns
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Chapter 16
Qinrong Feng, Duoqian Miao, Ruizhi Wang
Decision rules mining is an important technique in machine learning and data mining, it has been studied intensively during the past few years.... Sample PDF
Multidimensional Model-Based Decision Rules Mining
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About the Contributors