Knowledge discovery process and methods to enhance organizational performance
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 c...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Florida :
CRC Press
[2015]
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628929006719 |
Tabla de Contenidos:
- 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 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
