Data Mining: Foundations and Intelligent Paradigms
Volume 1: Clustering, Association and Classification
(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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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.
Bibliographische Angaben
- 2014, 2012, XVI, 336 Seiten, Maße: 15,8 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Dawn E. Holmes, Lakhmi C Jain
- Verlag: Springer, Berlin
- ISBN-10: 3642430937
- ISBN-13: 9783642430930
Sprache:
Englisch
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