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Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification de Dawn E. Holmes

Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification: Intelligent Systems Reference Library, cartea 23

Editat de Dawn E. Holmes, Lakhmi C Jain
en Limba Engleză Paperback – 26 ian 2014
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.
 
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Specificații

ISBN-13: 9783642430930
ISBN-10: 3642430937
Pagini: 352
Ilustrații: XVI, 336 p.
Dimensiuni: 155 x 235 x 18 mm
Greutate: 0.49 kg
Ediția:2012
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Intelligent Systems Reference Library

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 1of this three volume series, we have brought together contributions from some of the most prestigious researchers in the fundamental data mining tasks of clustering, association and classification. Each of the chapters is self contained. Theoreticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in these aspects of data mining.

Caracteristici

Latest research in data mining using intelligent paradigms and their applications State-of-the-Art title Written by leading experts in this field