Data Mining and Knowledge Discovery via Logic-Based Methods

1. Front Matter

   Pages i-xxxiii

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2. Algorithmic Issues

   1. Front Matter

      Pages 1-1

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   2. Introduction

      • Evangelos Triantaphyllou

      Pages 3-20

   3. Inferring a Boolean Function from Positive and Negative Examples

      • Evangelos Triantaphyllou

      Pages 21-56

   4. A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples

      • Evangelos Triantaphyllou

      Pages 57-72

   5. Some Fast Heuristics for Inferring a Boolean Function from Examples

      • Evangelos Triantaphyllou

      Pages 73-100

   6. An Approach to Guided Learning of Boolean Functions

      • Evangelos Triantaphyllou

      Pages 101-123

   7. An Incremental Learning Algorithm for Inferring Boolean Functions

      • Evangelos Triantaphyllou

      Pages 125-145

   8. A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples

      • Evangelos Triantaphyllou

      Pages 147-150

   9. The Rejectability Graph of Two Sets of Examples

      • Evangelos Triantaphyllou

      Pages 151-170

3. Application Issues

   1. Front Matter

      Pages 171-171

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   2. The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis

      • Evangelos Triantaphyllou

      Pages 173-190

   3. Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions

      • Evangelos Triantaphyllou

      Pages 191-227

   4. Some Application Issues of Monotone Boolean Functions

      • Evangelos Triantaphyllou

      Pages 229-239

   5. Mining of Association Rules

      • Evangelos Triantaphyllou

      Pages 241-255

   6. Data Mining of Text Documents

      • Evangelos Triantaphyllou

      Pages 257-276

   7. First Case Study: Predicting Muscle Fatigue from EMG Signals

      • Evangelos Triantaphyllou

      Pages 277-287

   8. Second Case Study: Inference of Diagnostic Rules for Breast Cancer

      • Evangelos Triantaphyllou

      Pages 289-296

   9. A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis

      • Evangelos Triantaphyllou

      Pages 297-308

   10. Conclusions

       • Evangelos Triantaphyllou

       Pages 309-315

The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

• Artifical Intelligence
• Boolean function
• Data Analysis
• Decision Making
• Intelligent Systems
• Knowledge Discovery
• Learning Systems
• algorithms
• data mining
• logic
• mathematical logic

• , Department of Computer Science, Louisiana State University, Baton Rouge, USA

  Evangelos Triantaphyllou

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