Data Mining in Crystallography
Hofmann, Detlef W. M. (EDT); Kuleshova, Liudmila N. (EDT); Apostolakis, J. (CON); Buchsbaum, C. (CON); Cheng, H. (CON)
ISBN 10: 3642047580 ISBN 13: 9783642047589
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Humans have been “manually” extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes’ theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: • Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. • Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. • Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. • Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys.
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Title: Data Mining in Crystallography
Publisher: Springer
Publication Date: 2009
Binding: Hardcover
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Humans have been ¿manually¿ extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes¿ theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: ¿ Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. ¿ Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. ¿ Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. ¿ Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys. Seller Inventory # 5912042/12
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Data Mining in Crystallography
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Data Mining in Crystallography
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This series presents critical reviews of the present position and future trends in modern chemical research concerned with chemical structure and bondingShort and concise reports, each written by the world s renowned expertsStill valid and . Seller Inventory # 5044413
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Data Mining in Crystallography
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Buch. Condition: Neu. Data Mining in Crystallography | D. W. M. Hofmann (u. a.) | Buch | xiv | Englisch | 2009 | Springer | EAN 9783642047589 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Seller Inventory # 101472573
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Data Mining in Crystallography (Structure and Bonding, 134)
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Data Mining in Crystallography
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Buch. Condition: Neu. Neuware -Humans have been ¿manually¿ extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes¿ theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: ¿ Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. ¿ Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. ¿ Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. ¿ Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch. Seller Inventory # 9783642047589
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Data Mining in Crystallography
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Humans have been 'manually' extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes' theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: - Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. - Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. - Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. - Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys. Seller Inventory # 9783642047589
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Data Mining in Crystallography
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ISBN 10: 3642047580
ISBN 13: 9783642047589
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Humans have been 'manually' extracting patterns from data for centuries, but the increasing volume of data in modern times has called for more automatic approaches. Early methods of identifying patterns in data include Bayes' theorem (1700s) and Regression analysis (1800s). The proliferation, ubiquity and incre- ing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has - creasingly been augmented with indirect, automatic data processing. Data mining has been developed as the tool for extracting hidden patterns from data, by using computing power and applying new techniques and methodologies for knowledge discovery. This has been aided by other discoveries in computer science, such as Neural networks, Clustering, Genetic algorithms (1950s), Decision trees (1960s) and Support vector machines (1980s). Data mining commonlyinvolves four classes of tasks: - Classi cation: Arranges the data into prede ned groups. For example, an e-mail program might attempt to classify an e-mail as legitimate or spam. Common algorithmsinclude Nearest neighbor,Naive Bayes classi er and Neural network. - Clustering: Is like classi cation but the groups are not prede ned, so the algorithm will try to group similar items together. - Regression: Attempts to nd a function which models the data with the least error. A common method is to use Genetic Programming. - Association rule learning: Searches for relationships between variables. For example, a supermarket might gather data of what each customer buys. 188 pp. Englisch. Seller Inventory # 9783642047589
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