Knowledge Discovery for Business Information Systems

. Ed(s): Abramowicz, Witold; Zurada, Jozef

ISBN 10: 0792372433 / ISBN 13: 9780792372431

Verlag: Kluwer Academic Publishers, 2000

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. This work contains a collection of 16 articles written by experts in the KDD and DM field from the following countries: Austria, Australia, Canada, China (Hong Kong), and Germany. Editor(s): Abramowicz, Witold; Zurada, Jozef. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 450 pages, biography. BIC Classification: KJQ; UN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 25. Weight in Grams: 813. . 2000. Hardback. . . . . Bestandsnummer des Verkäufers V9780792372431

Inhaltsangabe:

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.

Reseña del editor: 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.

Bibliografische Details

Titel: Knowledge Discovery for Business Information...
Verlag: Kluwer Academic Publishers
Erscheinungsdatum: 2000
Einband: Hardcover
Zustand: New