Data Mining Methods and Applications

1. Auflage 2007
ISBN: 978-0-8493-8522-3
Verlag: Taylor & Francis Ltd

Buch, Englisch, 332 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 612 g

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

Zielgruppe

Undergraduate

Autoren/Hrsg.

Lawrence, Kenneth D.

Kudyba, Stephan

Klimberg, Ronald K.

Fachgebiete

• Mathematik | Informatik Mathematik Mathematik Allgemein

Inhaltsverzeichnis

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

Über Autor(innen)

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