1st Edition

Data Mining Methods and Applications

    332 Pages 50 B/W Illustrations
    by Auerbach Publications

    With today’s information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them.

    Gain a Competitive Advantage

    • Employ data mining in research and forecasting
    • Build models with data management tools and methodology optimization
    • Gain sophisticated breakdowns and complex analysis through multivariate, evolutionary, and neural net methods
    • Learn how to classify data and maintain quality

    Transform Data into Business Acumen

    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

    An Approach to Analyzing and Modeling Systems for Real-Time Decisions, J. C. Brocklebank, T.Lehman, T. Grant,, R. Burgess, L. Nagar, H. Mukherjee, J.Dadhich, and P. Chaklanobish
    Ensemble Strategies for Neural Network Classifiers, P. Mangiameli and D. West
    Neural Network Classification with Uneven Misclassification, Costs and Imbalanced Group Sizes, J. Lan, M. Y. Hu, E. Patuwo, and G. P. Zhang
    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-makes the competitive advantage in an ever-evolving marketplace.
    Data Cleansing with Independent Component Analysis, G. Zeng and M. J. Embrechts
    A Multiple Criteria Approach to Creating Good Teams over Time, R.K. Klimberg , K. J. Boyle, and I. Yermish
    Data Mining Applications in Higher Education, C. M. Davis, J. M. Hardin, T. Bohannon, and Jerry Oglesby
    Data Mining for Market Segmentation with Market Share Data
    A Case Study Approach
    I. Mowerman and S. J. Lloyd
    An Enhancement of the Pocket Algorithm
    with Ratche for Use in Data Mining Applications
    L. W. Glorfeld and D. White
    Identification and Prediction of Chronic Conditions
    for Health Plan Members Using Data Mining Techniques
    T.L. Perry, S. Kudyba, and K. Lawrence
    Monitoring and Managing Data and Process Quality Using Data Mining: Business Process Management for the Purchasing and Accounts Payable Processes, D.E. O’Leary
    Data Mining for Individual Consumer Models and Personalized Retail Promotions, R. Ghani, C. Cumby, A. Fano, and M. Krema
    Data Mining Common Definitions, Applications, and Misunderstandings, R.D. Pollack
    Fuzzy Sets in Data Mining and Ordinal Classification, D. L. Olson, H. Moshkovich and, A. Mechitov
    Developing an Associative Keyword Space of the Data Mining Literature through Latent Semantic Analysis, A. Gardiner
    A Classification Model for a Two-Class (New Product Purchase) Discrimination Process using Multiple-Criteria Linear Programming, K.D. Lawrence, D. R. Pai, R.K. Klimberg, S. Kudyba and, S. M. Lawrence


    Kenneth D. Lawrence, Stephan Kudyba, Ronald K. Klimberg