This book examines data mining research, demonstrating diverse uses of techniques and their applications for data mining. The chapters illustrate applications of data mining and visualization for credit screening, forecasting, medical diagnosis, genetic algorithms, Bayesian networks, neural...

We embarked on a case study to explore one organization’s experiences with radical change for the purpose of uncovering how they achieved success. The organization we examined was Honeywell Inc. in Phoenix, Arizona, USA. From the interview data, we were able to devise a set of ten lessons to...

In this paper, we use the market asset disclaimer assumption and develop a binomial lattice based real options model to include cash flow interdependencies between multi-stage information technology (IT) investments. Using a simple two-stage IT investment problem with interdependent cash flows,...

We propose a hybrid evolutionary-neural approach for binary classification that incorporates a special training data over-fitting minimizing selection procedure for improving the prediction accuracy on holdout sample. Our approach integrates parallel global search capability of genetic...

Conditional probability tables (CPT) in many Bayesian networks often contain missing values. The problem of missing values in CPT is a very common problem and occurs due to the lack of data on certain scenarios that are observed in the real world but are missing in the training data. The current...

A major step in effectively managing radio resources in a cellular network is to design an appropriate scheme for assigning cells to a location area (LA), serviced by a switch, and allocate resources for individual switches. However, this assignment is already proven in the literature to be an...

We propose a methodology that uses data envelopment analysis (DEA) for solving the inverse classification problem. An inverse classification problem involves finding out how predictor attributes of a case can be changed so that the case can be classified into a different and more desirable...