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Knowledge Discovery Process and Methods to Enhance Organizational Performance
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Knowledge Discovery Process and Methods to Enhance Organizational Performance

Knowledge Discovery Process and Methods to Enhance Organizational Performance

Edited ByKweku-Muata Osei-Bryson, Corlane Barclay
Edition 1st Edition
First Published 2015
eBook Published 16 March 2015
Pub. location New York
Imprint Auerbach Publications
DOIhttps://doi.org/10.1201/b18231
Pages 404 pages
eBook ISBN 9781482212389
SubjectsComputer Science, Economics, Finance, Business & Industry, Politics & International Relations
Get Citation

Get Citation

Osei-Bryson, K.M. (Ed.), Barclay, C. (Ed.). (2015). Knowledge Discovery Process and Methods to Enhance Organizational Performance. New York: Auerbach Publications, https://doi.org/10.1201/b18231
ABOUT THIS BOOK

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id

TABLE OF CONTENTS
chapter 1|10 pages
Introduction
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 2|14 pages
Overview of Knowledge Discovery and Data Mining Process Models
BySUMANA SHARMA
View abstract
chapter 3|30 pages
An Integrated Knowledge Discovery and Data Mining Process Model
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 4|28 pages
A Novel Method for Formulating the Business Objectives of Data Mining Projects
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 5|20 pages
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
ByCORLANE BARCLAY
View abstract
chapter 6|20 pages
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 7|20 pages
Issues and Considerations in the Application of Data Mining in Business
ByEDWARD CHEN
View abstract
chapter 8|22 pages
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
ByPATRICIA E. NALWOGA LUTU
View abstract
chapter 9|24 pages
Critical Success Factors in Knowledge Discovery and Data Mining Projects
ByCORLANE BARCLAY
View abstract
chapter 10|26 pages
Data Mining for Organizations: Challenges and Opportunities for Small Developing States
ByCORLANE BARCLAY
View abstract
chapter 11|24 pages
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
BySERGEY SAMOILENKO AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 12|20 pages
Applications of Data Mining in Organizational Behavior
ByARASH SHAHIN, REZA SALEHZADEH
View abstract
chapter 13|20 pages
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 14|26 pages
Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
ByCORLANE BARCLAY, ANDREW DENNIS, JEROME SHEPHERD
View abstract
chapter 15|22 pages
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 16|20 pages
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 17|30 pages
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
ByWAAD BOUAGUEL, GHAZI BEL MUFTI, AND MOHAMED LIMAM
View abstract

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id

TABLE OF CONTENTS
chapter 1|10 pages
Introduction
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 2|14 pages
Overview of Knowledge Discovery and Data Mining Process Models
BySUMANA SHARMA
View abstract
chapter 3|30 pages
An Integrated Knowledge Discovery and Data Mining Process Model
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 4|28 pages
A Novel Method for Formulating the Business Objectives of Data Mining Projects
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 5|20 pages
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
ByCORLANE BARCLAY
View abstract
chapter 6|20 pages
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 7|20 pages
Issues and Considerations in the Application of Data Mining in Business
ByEDWARD CHEN
View abstract
chapter 8|22 pages
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
ByPATRICIA E. NALWOGA LUTU
View abstract
chapter 9|24 pages
Critical Success Factors in Knowledge Discovery and Data Mining Projects
ByCORLANE BARCLAY
View abstract
chapter 10|26 pages
Data Mining for Organizations: Challenges and Opportunities for Small Developing States
ByCORLANE BARCLAY
View abstract
chapter 11|24 pages
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
BySERGEY SAMOILENKO AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 12|20 pages
Applications of Data Mining in Organizational Behavior
ByARASH SHAHIN, REZA SALEHZADEH
View abstract
chapter 13|20 pages
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 14|26 pages
Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
ByCORLANE BARCLAY, ANDREW DENNIS, JEROME SHEPHERD
View abstract
chapter 15|22 pages
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 16|20 pages
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 17|30 pages
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
ByWAAD BOUAGUEL, GHAZI BEL MUFTI, AND MOHAMED LIMAM
View abstract
CONTENTS
ABOUT THIS BOOK

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id

TABLE OF CONTENTS
chapter 1|10 pages
Introduction
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 2|14 pages
Overview of Knowledge Discovery and Data Mining Process Models
BySUMANA SHARMA
View abstract
chapter 3|30 pages
An Integrated Knowledge Discovery and Data Mining Process Model
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 4|28 pages
A Novel Method for Formulating the Business Objectives of Data Mining Projects
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 5|20 pages
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
ByCORLANE BARCLAY
View abstract
chapter 6|20 pages
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 7|20 pages
Issues and Considerations in the Application of Data Mining in Business
ByEDWARD CHEN
View abstract
chapter 8|22 pages
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
ByPATRICIA E. NALWOGA LUTU
View abstract
chapter 9|24 pages
Critical Success Factors in Knowledge Discovery and Data Mining Projects
ByCORLANE BARCLAY
View abstract
chapter 10|26 pages
Data Mining for Organizations: Challenges and Opportunities for Small Developing States
ByCORLANE BARCLAY
View abstract
chapter 11|24 pages
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
BySERGEY SAMOILENKO AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 12|20 pages
Applications of Data Mining in Organizational Behavior
ByARASH SHAHIN, REZA SALEHZADEH
View abstract
chapter 13|20 pages
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 14|26 pages
Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
ByCORLANE BARCLAY, ANDREW DENNIS, JEROME SHEPHERD
View abstract
chapter 15|22 pages
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 16|20 pages
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 17|30 pages
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
ByWAAD BOUAGUEL, GHAZI BEL MUFTI, AND MOHAMED LIMAM
View abstract

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id

TABLE OF CONTENTS
chapter 1|10 pages
Introduction
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 2|14 pages
Overview of Knowledge Discovery and Data Mining Process Models
BySUMANA SHARMA
View abstract
chapter 3|30 pages
An Integrated Knowledge Discovery and Data Mining Process Model
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 4|28 pages
A Novel Method for Formulating the Business Objectives of Data Mining Projects
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 5|20 pages
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
ByCORLANE BARCLAY
View abstract
chapter 6|20 pages
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 7|20 pages
Issues and Considerations in the Application of Data Mining in Business
ByEDWARD CHEN
View abstract
chapter 8|22 pages
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
ByPATRICIA E. NALWOGA LUTU
View abstract
chapter 9|24 pages
Critical Success Factors in Knowledge Discovery and Data Mining Projects
ByCORLANE BARCLAY
View abstract
chapter 10|26 pages
Data Mining for Organizations: Challenges and Opportunities for Small Developing States
ByCORLANE BARCLAY
View abstract
chapter 11|24 pages
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
BySERGEY SAMOILENKO AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 12|20 pages
Applications of Data Mining in Organizational Behavior
ByARASH SHAHIN, REZA SALEHZADEH
View abstract
chapter 13|20 pages
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 14|26 pages
Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
ByCORLANE BARCLAY, ANDREW DENNIS, JEROME SHEPHERD
View abstract
chapter 15|22 pages
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 16|20 pages
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 17|30 pages
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
ByWAAD BOUAGUEL, GHAZI BEL MUFTI, AND MOHAMED LIMAM
View abstract
ABOUT THIS BOOK
ABOUT THIS BOOK

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id

TABLE OF CONTENTS
chapter 1|10 pages
Introduction
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 2|14 pages
Overview of Knowledge Discovery and Data Mining Process Models
BySUMANA SHARMA
View abstract
chapter 3|30 pages
An Integrated Knowledge Discovery and Data Mining Process Model
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 4|28 pages
A Novel Method for Formulating the Business Objectives of Data Mining Projects
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 5|20 pages
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
ByCORLANE BARCLAY
View abstract
chapter 6|20 pages
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 7|20 pages
Issues and Considerations in the Application of Data Mining in Business
ByEDWARD CHEN
View abstract
chapter 8|22 pages
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
ByPATRICIA E. NALWOGA LUTU
View abstract
chapter 9|24 pages
Critical Success Factors in Knowledge Discovery and Data Mining Projects
ByCORLANE BARCLAY
View abstract
chapter 10|26 pages
Data Mining for Organizations: Challenges and Opportunities for Small Developing States
ByCORLANE BARCLAY
View abstract
chapter 11|24 pages
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
BySERGEY SAMOILENKO AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 12|20 pages
Applications of Data Mining in Organizational Behavior
ByARASH SHAHIN, REZA SALEHZADEH
View abstract
chapter 13|20 pages
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 14|26 pages
Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
ByCORLANE BARCLAY, ANDREW DENNIS, JEROME SHEPHERD
View abstract
chapter 15|22 pages
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 16|20 pages
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 17|30 pages
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
ByWAAD BOUAGUEL, GHAZI BEL MUFTI, AND MOHAMED LIMAM
View abstract

Although the terms "data mining" and "knowledge discovery and data mining" (KDDM) are sometimes used interchangeably, data mining is actually just one step in the KDDM process. Data mining is the process of extracting useful information from data, while KDDM is the coordinated process of understanding the business and mining the data in order to id

TABLE OF CONTENTS
chapter 1|10 pages
Introduction
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 2|14 pages
Overview of Knowledge Discovery and Data Mining Process Models
BySUMANA SHARMA
View abstract
chapter 3|30 pages
An Integrated Knowledge Discovery and Data Mining Process Model
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 4|28 pages
A Novel Method for Formulating the Business Objectives of Data Mining Projects
BySUMANA SHARMA AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 5|20 pages
The Application of the Business Understanding Phase of the CRISP-DM Approach to a Knowledge Discovery Project on Education
ByCORLANE BARCLAY
View abstract
chapter 6|20 pages
A Context-Aware Framework for Supporting the Evaluation of Data Mining Results
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 7|20 pages
Issues and Considerations in the Application of Data Mining in Business
ByEDWARD CHEN
View abstract
chapter 8|22 pages
The Importance of Data Quality Assurance to the Data Analysis Activities of the Data Mining Process
ByPATRICIA E. NALWOGA LUTU
View abstract
chapter 9|24 pages
Critical Success Factors in Knowledge Discovery and Data Mining Projects
ByCORLANE BARCLAY
View abstract
chapter 10|26 pages
Data Mining for Organizations: Challenges and Opportunities for Small Developing States
ByCORLANE BARCLAY
View abstract
chapter 11|24 pages
Determining Sources of Relative Inefficiency in Heterogeneous Samples Using Multiple Data Analytic Techniques
BySERGEY SAMOILENKO AND KWEKU-MUATA OSEI-BRYSON
View abstract
chapter 12|20 pages
Applications of Data Mining in Organizational Behavior
ByARASH SHAHIN, REZA SALEHZADEH
View abstract
chapter 13|20 pages
Decision Making and Decision Styles of Project Managers: A Preliminary Exploration Using Data Mining Techniques
ByKWEKU-MUATA OSEI-BRYSON AND CORLANE BARCLAY
View abstract
chapter 14|26 pages
Application of the CRISP-DM Model in Predicting High School Students’ Examination (CSEC/CXC) Performance
ByCORLANE BARCLAY, ANDREW DENNIS, JEROME SHEPHERD
View abstract
chapter 15|22 pages
Post-Pruning in Decision Tree Induction Using Multiple Performance Measures
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 16|20 pages
Selecting Classifiers for an Ensemble—An Integrated Ensemble Generation Procedure
ByKWEKU-MUATA OSEI-BRYSON
View abstract
chapter 17|30 pages
A New Feature Selection Technique Applied to Credit Scoring Data Using a Rank Aggregation Approach Based on Optimization, Genetic Algorithm, and Similarity
ByWAAD BOUAGUEL, GHAZI BEL MUFTI, AND MOHAMED LIMAM
View abstract
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