Data Mining Methods and Applications

Data Mining Methods and Applications

by Kenneth D. Lawrence, Stephan Kudyba
     
 

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

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Overview

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

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Product Details

ISBN-13:
9780849385223
Publisher:
Taylor & Francis
Publication date:
12/18/2007
Series:
Discrete Mathematics and Its Applications Series
Pages:
336
Product dimensions:
6.20(w) x 9.30(h) x 0.90(d)

Related Subjects

Table of Contents

TECHNIQUES OF DATA MINING
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
APPLICATIONS OF DATA MINING
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
OTHER AREAS OF DATA MINING
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

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