Mining Very Large Databases with Parallel Processing:
Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely 'intelligent' (machine learning-based) data mining techniques, relational d...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer US
2000
|
| Ausgabe: | 1st ed. 2000 |
| Schriftenreihe: | Advances in Database Systems
9 |
| Schlagworte: | |
| Online-Zugang: | UBY01 Volltext |
| Zusammenfassung: | Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely 'intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning |
| Beschreibung: | 1 Online-Ressource (XIII, 208 p) |
| ISBN: | 9781461555216 |
| DOI: | 10.1007/978-1-4615-5521-6 |
Internformat
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| 520 | |a Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely 'intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning | ||
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| edition | 1st ed. 2000 |
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| isbn | 9781461555216 |
| language | English |
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| spelling | Freitas, Alex A. Verfasser aut Mining Very Large Databases with Parallel Processing by Alex A. Freitas, Simon H. Lavington 1st ed. 2000 New York, NY Springer US 2000 1 Online-Ressource (XIII, 208 p) txt rdacontent c rdamedia cr rdacarrier Advances in Database Systems 9 Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely 'intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning Data Structures and Information Theory Natural Language Processing (NLP) Data structures (Computer science) Natural language processing (Computer science) Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s DE-604 Lavington, Simon H. aut Erscheint auch als Druck-Ausgabe 9781461375234 Erscheint auch als Druck-Ausgabe 9780792380481 Erscheint auch als Druck-Ausgabe 9781461555223 https://doi.org/10.1007/978-1-4615-5521-6 Verlag URL des Eerstveröffentlichers Volltext |
| spellingShingle | Freitas, Alex A. Lavington, Simon H. Mining Very Large Databases with Parallel Processing Data Structures and Information Theory Natural Language Processing (NLP) Data structures (Computer science) Natural language processing (Computer science) Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4428654-5 |
| title | Mining Very Large Databases with Parallel Processing |
| title_auth | Mining Very Large Databases with Parallel Processing |
| title_exact_search | Mining Very Large Databases with Parallel Processing |
| title_exact_search_txtP | Mining Very Large Databases with Parallel Processing |
| title_full | Mining Very Large Databases with Parallel Processing by Alex A. Freitas, Simon H. Lavington |
| title_fullStr | Mining Very Large Databases with Parallel Processing by Alex A. Freitas, Simon H. Lavington |
| title_full_unstemmed | Mining Very Large Databases with Parallel Processing by Alex A. Freitas, Simon H. Lavington |
| title_short | Mining Very Large Databases with Parallel Processing |
| title_sort | mining very large databases with parallel processing |
| topic | Data Structures and Information Theory Natural Language Processing (NLP) Data structures (Computer science) Natural language processing (Computer science) Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data Structures and Information Theory Natural Language Processing (NLP) Data structures (Computer science) Natural language processing (Computer science) Data Mining |
| url | https://doi.org/10.1007/978-1-4615-5521-6 |
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