Data Mining in Crystallography by D W M Hofmann
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.-
Computational Methods for the Analysis of Non-Covalent Interactions
-
Structure and Bonding
-
Applications of Evolutionary Computation in Chemistry
-
Metallocofactors that Activate Small Molecules
-
Molecular Electronic Structures of Transition Metal Complexes I
-
Inorganic 3D Structures
-
Molecular Self-Assembly
-
Liquid Crystals I
-
Functional Phthalocyanine Molecular Materials
-
Fuel Cells and Hydrogen Storage
-
Photofunctional Transition Metal Complexes
-
Molecular Electronic Structures of Transition Metal Complexes II
-
Zintl Ions
-
Superconductivity in Complex Systems
-
Halogen Bonding
-
Zintl Phases
-
Less Common Metals in Proteins and Nucleic Acid Probes
-
Controlled Assembly and Modification of Inorganic Systems
-
Functional Molecular Silicon Compounds II
-
Structural Information from Spin-Labels and Intrinsic Paramagnetic Centres in the Biosciences
-
Functional Molecular Silicon Compounds I
-
Intermolecular Forces and Clusters II
-
Structures and Interactions of Ionic Liquids
-
Applications of Density Functional Theory to Chemical Reactivity
-
Molecular Machines and Motors
-
Structure-Property Relationships in Non-Linear Optical Crystals II
-
Metal-Metal Bonding
-
Organometallic and Coordination Chemistry of the Actinides
-
Molecular Catalysis of Rare-Earth Elements
-
Single-Molecule Magnets and Related Phenomena
-
Polyoxometalate-Based Assemblies and Functional Materials
-
Electron Density and Chemical Bonding II
-
Applications of Density Functional Theory to Biological and Bioinorganic Chemistry
-
Optical Spectra and Chemical Bonding in Transition Metal Complexes
-
Optical Spectra and Chemical Bonding in Inorganic Compounds
-
50th Anniversary of Electron Counting Paradigms for Polyhedral Molecules
-
Nitrosyl Complexes in Inorganic Chemistry, Biochemistry and Medicine II
| SKU | Unavailable |
| ISBN 13 | 9783642261619 |
| ISBN 10 | 3642261612 |
| Title | Data Mining in Crystallography |
| Author | D W M Hofmann |
| Series | Structure And Bonding |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer |
| Year published | 2012-03-14 |
| Number of pages | 172 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |