The Springer International Engineering and Computer Science

By: Witold Abramowicz (Editor), Jozef M Zurada (Editor)

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Hardcover | 30 November 2000

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Hardcover
456 Pages

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23.5 x 15.24 x 2.54

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Current database technology and computer hardware allow us to gather, store, access, and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition, the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless, our ability to discover critical, non-obvious nuggets of useful information in data that could influence or help in the decision making process, is still limited.
Knowledge discovery (KDD) and Data Mining (DM) is a new, multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory, machine learning, pattern recognition, statistics, artificial intelligence, uncertainty management, and high-performance computing.
To remain competitive, businesses must apply data mining techniques such as classification, prediction, and clustering using tools such as neural networks, fuzzy logic, and decision trees to facilitate making strategic decisions on a daily basis.
Knowledge Discovery for Business Information Systems contains a collection of 16 high quality articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Bulgaria, Canada, China (Hong Kong), Estonia, Denmark, Germany, Italy, Poland, Singapore and USA.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems Business & Management Business Mathematics & Systems Computer Programming & Software Development Algorithms & Data Structures

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Paperback

Published: 7th April 2013

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Preface
Foreword
List of Contributors
Information Filters Supplying Data Warehouses with Benchmarking Information
Parallel Mining of Association Rules
Unsupervised Feature Ranking and Selection
Approaches to Concept Based Exploration of Information Resources
Hybrid Methodology of Knowledge Discovery for Business Information
Fuzzy Linguistic Summaries of Databases for an Efficient Business Data Analysis and Decision Support
Integrating Data Sources Using a Standardized Global Dictionary
Maintenance of Discovered Association Rules
Multidimensional Business Process Analysis with the Process Warehouse
Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeling
Robust Business Intelligence Solutions
The Role of Granular Information in Knowledge Discovery in Databases
Dealing with Dimensions in Data Warehousing
Enhancing the KDD Process in the Relational Database Mining Framework by Quantitative Evaluation of Association Rules
Speeding up Hypothesis Development
Sequence Mining in Dynamic and Interactive Environments
Investigation of Artificial Neural Networks for Classifying Levels of Financial Distress of Firms: The Case of an Unbalanced Training Sample
Index
Table of Contents provided by Publisher. All Rights Reserved.

The Springer International Engineering and Computer Science

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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