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Buch, Englisch, 336 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 534 g
Volume 1: Clustering, Association and Classification
Buch, Englisch, 336 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 534 g
Reihe: Intelligent Systems Reference Library
ISBN: 978-3-642-43093-0
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
