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Record Nr. |
UNINA9910800185203321 |
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Titolo |
Knowledge discovery process and methods : to enhance organizational performance / / edited by Kweku-Muata Osei-Bryson, Virginia Commonwealth University, School of Business, Corlane Barclay, University of Technology, Jamaica |
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Pubbl/distr/stampa |
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Boca Raton, Florida : , : CRC Press, , [2015] |
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©2015 |
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ISBN |
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1-138-89425-7 |
0-429-16099-2 |
1-4822-1238-2 |
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Edizione |
[1st edition] |
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Descrizione fisica |
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1 online resource (398 p.) |
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Disciplina |
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Soggetti |
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Database management |
Database searching |
Data mining |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Front Cover; Contents; Preface; Editors; Contributors; Chapter 1: Introduction; Chapter 2: Overview of Knowledge Discovery and Data Mining Process Models; Chapter 3: An Integrated Knowledge Discovery and Data Mining Process Model; Chapter 4: A Novel Method for Formulating the Business Objectives of Data Mining Projects; Chapter 5: The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education; Chapter 6: A Context-Aware Framework for Supporting the Evaluation of Data Mining Results |
Chapter 7: Issues and Considerations in the Application of Data Mining in BusinessChapter 8: The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process; Chapter 9: Critical Success Factors in Knowledge Discovery and Data Mining Projects; Chapter 10: Data Mining for Organizations: Challenges and Opportunities for Small Developing States; Chapter 11: Determining |
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Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques; Chapter 12: Applications of Data Mining in Organizational Behavior |
Chapter 13: Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining TechniquesChapter 14: Application of the CRISP-DM Model in Predicting High School Students' Examination (CSEC/CXC) Performance; Chapter 15: Post-Pruning in Decision Tree Induction Using Multiple Performance Measures; Chapter 16: Selecting Classifiers for an Ensemble-An Integrated Ensemble Generation Procedure; Chapter 17: A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity |
Back Cover |
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Sommario/riassunto |
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This book offers insights into the scope of data mining initiatives, including their socio-economic and legal implications to stakeholders, organizations, and society. There is a current paucity of literature with emphasis on developing countries or relatable cases with relevance to their specific contexts. Most current publications focus on technical and mathematical jargon without clear explanation of how organizations can implement KDDM. Filling this need, this book considers important trends, techniques, strategies, and best practices to help readers make the most of their organizational d |
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