Variations on Associative Classifiers and Classification Results Analyses

Variations on Associative Classifiers and Classification Results Analyses

Maria-Luiza Antonie (University of Alberta, Canada), David Chodos (University of Alberta, Canada) and Osmar Zaïane (University of Alberta, Canada)
DOI: 10.4018/978-1-60566-404-0.ch009
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Abstract

The chapter introduces the associative classifier, a classification model based on association rules, and describes the three phases of the model building process: rule generation, pruning, and selection. In the first part of the chapter, these phases are described in detail, and several variations on the associative classifier model are presented within the context of the relevant phase. These variations are: mining data sets with re-occurring items, using negative association rules, and pruning rules using graph-based techniques. Each of these departs from the standard model in a crucial way, and thus expands the classification potential. The second part of the chapter describes a system, ARC-UI that allows a user to analyze the results of classifying an item using an associative classifier. This system uses an intuitive, Web-based interface and, with this system, the user is able to see the rules that were used to classify an item, modify either the item being classified or the rule set that was used, view the relationship between attributes, rules and classes in the rule set, and analyze the training data set with respect to the item being classified.
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Introduction

The process of creating an associative classifier from a training data set has three main phases: (1) mining the training data for association rules and keeping only those that can classify instances, (2) pruning the mined rules to weed out irrelevant or noisy rules, and (3) selecting and combining the rules to classify unknown items. Within each of these steps, there is a great deal of potential for variation and improvement. The first three sections describe each of the three phases of the associative classification process in detail. In addition, three variations on this process are described, each within the context of the relevant classification phase. Each of these variations are outlined briefly in the following paragraphs and described using a running example of a department store sales dataset. To put the preceding paragraph into this context, we can imagine a store with data from previous months on the sales of various items in the store, and an assessment from a manager of whether the items were worth stocking. An associative classifier for this context would create a set of rules relating items’ sales figures to their overall profitability, and allow the manager to assess the current month’s stock based on data accumulated from previous months.

The first variation considers data sets with re-occurring items. Associative classifiers are typically concerned only with the presence of an attribute, which ignores potentially valuable information about the number of occurrences of that attribute. For example, in a text classification context, the number of occurrences of a word in a document and in a collection are crucial indicators of its importance. Or, to use the department store example, knowing how many shirts were sold might be more important than knowing whether or not any shirts were sold. A classification model by Rak et al (Rak, 2005) considers the number of occurrences of an attribute both in generating rules and in classifying items according to those rules. In the rule generation phase, the model increments a rule’s support by an amount proportional to the number of attribute occurrences. In the item classification phase, the model uses the Cosine Measure to measure the similarity between an item and rules which have re-occurring attributes.

The second variation presents a classifier which works with both positive and negative rules. Negative rules either use attribute/negated value pairs, or imply a negative classification, and can capture patterns and relationships that would be missed by positive only rule-based associative classifiers. In the department store context, knowing that a store did not sell any shirts of a certain brand could help a manager decide not to stock more shirts of that brand. Generating a complete set of negative association rules from a set of positive rules is a very difficult task, and can result in an exponential growth in the number of association rules. A method developed by Antonie and Zaïane (Antonie, 2004c) deals with this issue in two ways. First, the negative rules generated are restricted to those where either the entire antecedent or consequent is negated. Thus, a rule that identifies one brand of shirt that did not sell and another that did would not be generated using this method. Second, the correlation coefficient between a pattern and a frequent itemset is used to guide the generation of negative association rules. This method also incorporates both negative and positive rules into the item classification process.

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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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