cover of Data Mining Methods and Applications

Data Mining Methods and Applications

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

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About the Book

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

    Table of Contents

    TECHNIQUES OF DATA MINING
    An Approach to Analyzing and Modeling Systems
    for Real-Time Decisions
    Ensemble Strategies for Neural Network Classifiers
    Neural Network Classification with Uneven Misclassification
    Costs and Imbalanced Group Sizes
    Data Cleansing with Independent Component Analysis
    A Multiple Criteria Approach to Creating Good Teams over Time
    APPLICATIONS OF DATA MINING
    Data Mining Applications in Higher Education
    Data Mining for Market Segmentation with Market Share Data
    A Case Study Approach
    An Enhancement of the Pocket Algorithm
    with Ratche for Use in Data Mining Applications
    Identification and Prediction of Chronic Conditions
    for Health Plan Members Using Data Mining Techniques
    Monitoring and Managing Data and Process Quality
    Using Data Mining: Business Process Management
    for the Purchasing and Accounts Payable Processes
    Data Mining for Individual Consumer Models and Personalized
    Retail Promotions
    OTHER AREAS OF DATA MINING
    Data Mining Common Definitions, Applications,
    and Misunderstandings
    Fuzzy Sets in Data Mining and Ordinal Classification
    Developing an Associative Keyword Space of the Data Mining
    Literature through Latent Semantic Analysis
    A Classification Model for a Two-Class (New Product Purchase)
    Discrimination Process using Multiple-Criteria
    Linear Programming
    Index