Knowledge discovery for business information systems / edited by Witold Abramowicz, Jozef Zurada.

Format:

Book

Contributor:

Abramowicz, Witold.

Zurada, Jozef, 1949-

Series:

Kluwer international series in engineering and computer science ; SECS 600.

The Kluwer international series in engineering and computer science ; SECS 600

Language:

English

Subjects (All):

Data mining.

Knowledge acquisition (Expert systems).

Database searching.

Physical Description:

1 online resource (450 p.)

Edition:

1st ed. 2002.

Place of Publication:

Boston : Kluwer Academic Publishers, c2001.

Language Note:

English

Summary:

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.

Contents:

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.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-280-20565-2

9786610205653

0-306-46991-X

OCLC:

50621254