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Data Mining in Crystallography

  • Book
  • © 2010

Overview

Editors:
  1. D. W. M. Hofmann

    1. FlexCryst, Uttenreuth, Germany

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    You can also search for this editor in PubMed   Google Scholar

  2. Liudmila N. Kuleshova

    1. Research & Development in Sardinia, Center for Advanced Studies,, Pula, Italy

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • This series presents critical reviews of the present position and future trends in modern chemical research concerned with chemical structure and bonding
  • Short and concise reports, each written by the world's renowned experts
  • Still valid and useful after 5 or 10 years
  • More information as well as the electronic version of the whole content available at: springerlink.com
  • Includes supplementary material: sn.pub/extras

Part of the book series: Structure and Bonding (STRUCTURE, volume 134)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xii
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  2. An Introduction to Data Mining

    • Joannis Apostolakis
    Pages 1-35

  3. Data Bases, the Base for Data Mining

    • Christian Buchsbaum, Sabine Hãhler-Schlimm, Silke Rehme
    Pages 37-58

  4. Data Mining and Inorganic Crystallography

    • Krishna Rajan
    Pages 59-87

  5. Data Mining in Organic Crystallography

    • Detlef W. M. Hofmann
    Pages 89-134

  6. Data Mining for Protein Secondary Structure Prediction

    • Haitao Cheng, Taner Z. Sen, Robert L. Jernigan, Andrzej Kloczkowski
    Pages 135-167

  7. Back Matter

    Pages 169-172
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Keywords

About this book

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.

Editors and Affiliations

  • FlexCryst, Uttenreuth, Germany

    D. W. M. Hofmann

  • Research & Development in Sardinia, Center for Advanced Studies,, Pula, Italy

    Liudmila N. Kuleshova

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