Data Mining: Foundations and Intelligent Paradigms
(Sprache: Englisch)
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled "DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification" we wish to introduce some of...
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Produktinformationen zu „Data Mining: Foundations and Intelligent Paradigms “
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled "DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification" we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Klappentext zu „Data Mining: Foundations and Intelligent Paradigms “
There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled "DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification" we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.
Inhaltsverzeichnis zu „Data Mining: Foundations and Intelligent Paradigms “
- Introductory Chapter- Clustering Analysis in Large Graphs with Rich Attributes
- Temporal Data Mining: Similarity-Profiled Association Pattern
- Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification
- Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets
- Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation
- Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters
- DepMiner: A method and a system for the extraction of significant dependencies
- Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries
- Text Clustering with Named Entities: A Model, Experimentation and Realization
- Regional Association Rule Mining and Scoping from Spatial Data
- Learning from Imbalanced Data: Evaluation Matters Clustering Analysis in Large Graphs with Rich Attributes
- Temporal Data Mining: Similarity-Profiled Association Pattern
- Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification
- Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets
- Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation
- Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters
- DepMiner: A method and a system for the extraction of significant dependencies
- Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries
- Text Clustering with Named Entities: A Model, Experimentation and Realization
- Regional Association Rule Mining and Scoping from Spatial Data
- Learning
Bibliographische Angaben
- XVI, 336 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Herausgegeben: Dawn E. Holmes, Lakhmi C. Jain
- Verlag: Springer Berlin
- ISBN-10: 3642231659
- ISBN-13: 9783642231650
- Erscheinungsdatum: 07.11.2011
Sprache:
Englisch
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