Data Mining Methods and Applications

Contents: Preface. I. Techniques of data mining: 1. An approach to analyzing and modeling systems for real-time decisions/John C. Brocklebank, Tom Lehman, Tom Grant, Rich Burgess, Lokesh Nagar, Himadri Mukherjee, Juee Dadhich and Pias Chaklanobish. 2. Ensemble strategies for neural network classifiers/Paul Mangiameli and David West. 3. Neural network classification with uneven misclassification costs and imbalanced group sizes/Jyhshyan Lan, Michael Y. Hu, Eddy  Patuwo and G. Peter Zhang. 4. Data cleansing with independent component analysis/Guangyin Zeng and Mark J. Embrechts. 5. A multiple criteria approach to creating good teams over time/Ronald K. Klimberg, Kevin J. Boyle and Ira Yermish. II. Applications of data mining: 6. Data mining applications in higher education/Cali M. Davis, J. Michael Hardin, Tom Bohannon and Jerry Oglesby. 7. Data mining for market segmentation with market share data: a case study approach/Illya Mowerman and Scott J. Iloyd. 8. An enhancement of the pocket algorithm with ratchet for use in data mining applications/Louis W. Glorfeld and Doug White. 9. Identification and prediction of chronic conditions for health plan members using data mining techniques/Theodore L. Perry, Stephan Kudyba, and Kenneth D. Lawrence. 10. Monitoring and managing data and process quality using data mining: business process management for the purchasing and accounts payable processes/Daniel E. O\'Leary. 11. Data mining for individual consumer models and personalized retail promotions/Rayid Ghani, Chad Cumby, Andrew Fano and Marko Krema. III. Other areas of data mining: 12. Data mining: common definitions, applications, and misunderstandings/Richard D. Pollack. 13. Fuzzy sets in data mining and ordinal classification/David L. Olson, Helen Moshkovich and Alexander Mechitov. 14. Developing an associative keyword space of the data mining literature through latent semantic analysis/Adrian Gardiner. 15. A classification model for a two-class (new product purchase) discrimination process using multiple-criteria linear programming/Kenneth D. Lawrence, Dinesh R. Pai, Ronald K. Klimberg, Stephan Kudyba and Sheila M. Lawrence. Index.

"Data Mining Methods and Applications supplies organizations with the data management tools that will allow them to harness the critical facts and figures needed to improve their bottom line. Drawing from finance, marketing, economics, science and healthcare, this forward thinking volume:

• Demonstrates how the transformation of data into business intelligence is an essential aspect of strategic decision-making. Emphasizes the use of data mining concepts in real-world scenarios with large database components.
• Focuses on data mining and forecasting methods in conducting market research.
• Balances theory with application in management, finance, marketing, economics, healthcare and science.
• Presents insights and cutting-edge methods from leading experts in data management.

To stay ahead of the competition, organizations must possess the skill set needed to make faster and more informed decisions. The data mining methods outlined in this text give savvy decision-makers the competitive advantage in an ever-evolving marketplace." (jacket)