Knowledge Discovery for Business Information Systems:
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,...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2002
|
| Schriftenreihe: | The International Series in Engineering and Computer Science
600 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 Online-Ressource (XVIII, 432 p) |
| ISBN: | 9780306469916 |
| DOI: | 10.1007/b116447 |
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| 520 | |a 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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| 650 | 4 | |a Data Structures, Cryptology and Information Theory | |
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Datensatz im Suchindex
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| dewey-hundreds | 000 - Computer science, information, general works |
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| discipline | Informatik |
| doi_str_mv | 10.1007/b116447 |
| format | Electronic eBook |
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| id | DE-604.BV045148413 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:01Z |
| institution | BVB |
| isbn | 9780306469916 |
| language | English |
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| oclc_num | 1050946590 |
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| physical | 1 Online-Ressource (XVIII, 432 p) |
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| publishDate | 2002 |
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| publisher | Springer US |
| record_format | marc |
| series2 | The International Series in Engineering and Computer Science |
| spelling | Knowledge Discovery for Business Information Systems edited by Witold Abramowicz, Jozef Zurada Boston, MA Springer US 2002 1 Online-Ressource (XVIII, 432 p) txt rdacontent c rdamedia cr rdacarrier The International Series in Engineering and Computer Science 600 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. Computer Science Data Structures, Cryptology and Information Theory IT in Business Artificial Intelligence (incl. Robotics) Computer science Information technology Business / Data processing Data structures (Computer science) Artificial intelligence Abramowicz, Witold edt Zurada, Jozef edt Erscheint auch als Druck-Ausgabe 9780792372431 https://doi.org/10.1007/b116447 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Knowledge Discovery for Business Information Systems Computer Science Data Structures, Cryptology and Information Theory IT in Business Artificial Intelligence (incl. Robotics) Computer science Information technology Business / Data processing Data structures (Computer science) Artificial intelligence |
| title | Knowledge Discovery for Business Information Systems |
| title_auth | Knowledge Discovery for Business Information Systems |
| title_exact_search | Knowledge Discovery for Business Information Systems |
| title_full | Knowledge Discovery for Business Information Systems edited by Witold Abramowicz, Jozef Zurada |
| title_fullStr | Knowledge Discovery for Business Information Systems edited by Witold Abramowicz, Jozef Zurada |
| title_full_unstemmed | Knowledge Discovery for Business Information Systems edited by Witold Abramowicz, Jozef Zurada |
| title_short | Knowledge Discovery for Business Information Systems |
| title_sort | knowledge discovery for business information systems |
| topic | Computer Science Data Structures, Cryptology and Information Theory IT in Business Artificial Intelligence (incl. Robotics) Computer science Information technology Business / Data processing Data structures (Computer science) Artificial intelligence |
| topic_facet | Computer Science Data Structures, Cryptology and Information Theory IT in Business Artificial Intelligence (incl. Robotics) Computer science Information technology Business / Data processing Data structures (Computer science) Artificial intelligence |
| url | https://doi.org/10.1007/b116447 |
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