ABSTRACT
CONTENTS 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 12.2 Military Manpower Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 12.3 Data Mining and Traditional Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 12.4 General Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 12.5 Attempted Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 12.6 Regression Specific Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 12.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Data mining has emerged in response to a need from industry for effective and efficient analysis of large data sets. It is used with great success in the business world in areas such as marketing. Traditional regression techniques have not generally been used in data mining but may be more attractive in some applications. An example of such an application is military manpower analysis. We introduce how we will approach this problem using logistic and linear regression.
