Knowledge Discovery and Data Mining: Challenges and Realities, 1st Edition

  • Published By:
  • ISBN-10: 1599042541
  • ISBN-13: 9781599042541
  • Grade Level Range: College Freshman - College Senior
  • 274 Pages | eBook
  • Original Copyright 2007 | Published/Released September 2007
  • This publication's content originally published in print form: 2007

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Knowledge discovery and data mining (KDD) is dedicated to extracting meaningful information from a large volume of data. Knowledge Discovery and Data Mining: Challenges and Realities is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This reference source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing, to provide library reference collections with a complete body of distinguished research.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Table of Contents.
Detailed Table of Contents.
1: Data Mining in Software Quality Modeling.
2: Software Quality Modeling with Limited Apriori Defect Data.
3: Knowledge Discovery from Genetic and Medical Data.
4: Genome-Wide Analysis of Epistasis Using Multifactor Dimensionality Reduction: Feature Selection and Construction in the Domain of Human Genetics.
5: Mining Clinical Trial Data.
6: Data Mining in Mixed Media Data.
7: Cross-Modal Correlation Mining Using Graph Algorithms.
8: Mining Image Data Repository.
9: Image Mining for the Construction of Semantic-Inference Rules and for the Development of Automatic Image Diagnosis Systems.
10: A Successive Decision Tree Approach to Mining Remotely Sensed Image Data.
11: Data Mining and Business Intelligence.
12: The Business Impact of Predictive Analytics.
13: Beyond Classification: Challenges of Data Mining for Credit Scoring.
14: Data Mining and Ontology Engineering.
15: Semantics Enhancing Knowledge Discovery and Ontology Engineering Using Mining Techniques: A Crossover Review.
16: Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies.
17: Traditional Data Mining Algorithms.
18: Effective Intelligent Data Mining Using Dempster-Shafer Theory.
19: Outlier Detection Strategy Using the Self-Organizing Map.
20: Re-Sampling Based Data Mining Using Rough Set Theory.
About the Authors.