LEADER 04233oam 2200661I 450 001 9910800185203321 005 20170822122609.0 010 $a1-138-89425-7 010 $a0-429-16099-2 010 $a1-4822-1238-2 024 7 $a10.1201/b18231 035 $a(CKB)2670000000601153 035 $a(EBL)1779396 035 $a(SSID)ssj0001494339 035 $a(PQKBManifestationID)11771588 035 $a(PQKBTitleCode)TC0001494339 035 $a(PQKBWorkID)11515814 035 $a(PQKB)10618776 035 $a(MiAaPQ)EBC1779396 035 $a(OCoLC)904755529 035 $a(CaSebORM)9781482212365 035 $a(EXLCZ)992670000000601153 100 $a20180331h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aKnowledge discovery process and methods $eto enhance organizational performance /$fedited by Kweku-Muata Osei-Bryson, Virginia Commonwealth University, School of Business, Corlane Barclay, University of Technology, Jamaica 205 $a1st edition 210 1$aBoca Raton, Florida :$cCRC Press,$d[2015] 210 4$dİ2015 215 $a1 online resource (398 p.) 300 $aAn Auerbach book. 311 $a1-4822-1236-6 311 $a1-336-19430-8 320 $aIncludes bibliographical references. 327 $aFront 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 327 $aChapter 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 327 $aChapter 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 327 $aBack Cover 330 $aThis 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 606 $aDatabase management 606 $aDatabase searching 606 $aData mining 615 0$aDatabase management. 615 0$aDatabase searching. 615 0$aData mining. 676 $a005.74/1 676 $a005.741 702 $aOsei-Bryson$b Kweku-Muata 702 $aBarclay$b Corlane 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910800185203321 996 $aKnowledge discovery process and methods$93874644 997 $aUNINA