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Author: D. W. M. Hofmann
Publisher: Springer
ISBN: 3642047599
Category : Science
Languages : en
Pages : 181
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Book Description
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.
Author: D. W. M. Hofmann
Publisher: Springer
ISBN: 3642047599
Category : Science
Languages : en
Pages : 181
Get Book Here
Book Description
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.
Author: D. W. M. Hofmann
Publisher: Springer Science & Business Media
ISBN: 3642047580
Category : Science
Languages : en
Pages : 181
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Book Description
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.
Author: Olexandr Isayev
Publisher: John Wiley & Sons
ISBN: 3527802258
Category : Technology & Engineering
Languages : en
Pages : 160
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Book Description
Provides everything readers need to know for applying the power of informatics to materials science There is a tremendous interest in materials informatics and application of data mining to materials science. This book is a one-stop guide to the latest advances in these emerging fields. Bridging the gap between materials science and informatics, it introduces readers to up-to-date data mining and machine learning methods. It also provides an overview of state-of-the-art software and tools. Case studies illustrate the power of materials informatics in guiding the experimental discovery of new materials. Materials Informatics: Methods, Tools and Applications is presented in two parts?Methodological Aspects of Materials Informatics and Practical Aspects and Applications. The first part focuses on developments in software, databases, and high-throughput computational activities. Chapter topics include open quantum materials databases; the ICSD database; open crystallography databases; and more. The second addresses the latest developments in data mining and machine learning for materials science. Its chapters cover genetic algorithms and crystal structure prediction; MQSPR modeling in materials informatics; prediction of materials properties; amongst others. -Bridges the gap between materials science and informatics -Covers all the known methodologies and applications of materials informatics -Presents case studies that illustrate the power of materials informatics in guiding the experimental quest for new materials -Examines the state-of-the-art software and tools being used today Materials Informatics: Methods, Tools and Applications is a must-have resource for materials scientists, chemists, and engineers interested in the methods of materials informatics.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author:
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110476622
Category : Science
Languages : en
Pages : 172
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Book Description
Zeitschrift für Kristallographie. Supplement Volume 36 presents the complete Abstracts of all contributions to the 24th Annual Conference of the German Crystallographic Society in Stuttgart (Germany) 2016: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.
Author: Marc L. Pusey
Publisher: Springer
ISBN: 3319589377
Category : Computers
Languages : en
Pages : 245
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Book Description
This unique text/reference presents an overview of the computational aspects of protein crystallization, describing how to build robotic high-throughput and crystallization analysis systems. The coverage encompasses the complete data analysis cycle, including the set-up of screens by analyzing prior crystallization trials, the classification of crystallization trial images by effective feature extraction, the analysis of crystal growth in time series images, the segmentation of crystal regions in images, the application of focal stacking methods for crystallization images, and the visualization of trials. Topics and features: describes the fundamentals of protein crystallization, and the scoring and categorization of crystallization image trials; introduces a selection of computational methods for protein crystallization screening, and the hardware and software architecture for a basic high-throughput system; presents an overview of the image features used in protein crystallization classification, and a spatio-temporal analysis of protein crystal growth; examines focal stacking techniques to avoid blurred crystallization images, and different thresholding methods for binarization or segmentation; discusses visualization methods and software for protein crystallization analysis, and reviews alternative methods to X-ray diffraction for obtaining structural information; provides an overview of the current challenges and potential future trends in protein crystallization. This interdisciplinary work serves as an essential reference on the computational and data analytics components of protein crystallization for the structural biology community, in addition to computer scientists wishing to enter the field of protein crystallization.
Author: Deutsches Elektronen-Synchrotron-DESY
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110740222
Category : Science
Languages : en
Pages : 213
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Book Description
Zeitschrift für Kristallographie. Supplement Volume 41 presents the complete Abstracts of all contributions to the 29th Annual Conference of the German Crystallographic Society in Hamburg (Germany) 2021: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.
Author: Krishna Rajan
Publisher: Elsevier Inc. Chapters
ISBN: 0128059451
Category : Technology & Engineering
Languages : en
Pages : 31
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Book Description
This chapter provides a discussion of how informatics tools can address one of the fundamental historical developments in crystal chemistry, that of structure maps. Such maps serve as a means to explore how specific parameters associated with crystal and electronic structure can serve as a way to rationalize groupings, or classifications, relating structure and chemistry. Historically, structure maps have evolved through a variety of heuristic approaches that define a priori how parameters may be important, and then classifications are discovered as one populates these maps with data. The resulting clustering of data serves as a heuristic tool to rationalize new discoveries and new structure–bonding relationships.
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Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110415372
Category : Science
Languages : en
Pages : 156
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Book Description
Zeitschrift für Kristallographie. Supplement Volume 35 presents the complete Abstracts of all contributions to the 23rd Annual Conference of the German Crystallographic Society in Göttingen (Germany) 2015: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.
Author:
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110657279
Category : Science
Languages : en
Pages : 134
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Book Description
Zeitschrift für Kristallographie. Supplement Volume 39 presents the complete Abstracts of all contributions to the 27th Annual Conference of the German Crystallographic Society in Leipzig (Germany) 2019: - Plenary Talks - Microsymposia - Poster Session Supplement Series of Zeitschrift für Kristallographie publishes Abstracts of international conferences on the interdisciplinary field of crystallography.
