Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification

Author:   Dawn E. Holmes ,  Lakhmi C Jain
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   2012 ed.
Volume:   23
ISBN:  

9783642430930


Pages:   336
Publication Date:   26 January 2014
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Data Mining: Foundations and Intelligent Paradigms: Volume 1:  Clustering, Association and Classification


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Overview

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.  

Full Product Details

Author:   Dawn E. Holmes ,  Lakhmi C Jain
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2012 ed.
Volume:   23
Dimensions:   Width: 15.50cm , Height: 1.90cm , Length: 23.50cm
Weight:   0.539kg
ISBN:  

9783642430930


ISBN 10:   3642430937
Pages:   336
Publication Date:   26 January 2014
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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