Dawn E. Holmes & Lakhmi C Jain 
Data Mining: Foundations and Intelligent Paradigms [PDF ebook] 
Volume 1: Clustering, Association and Classification

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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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表中的内容

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.- Dep Miner: 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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语言 英语 ● 格式 PDF ● 网页 336 ● ISBN 9783642231667 ● 文件大小 5.9 MB ● 编辑 Dawn E. Holmes & Lakhmi C Jain ● 出版者 Springer Berlin ● 市 Heidelberg ● 国家 DE ● 发布时间 2011 ● 下载 24 个月 ● 货币 EUR ● ID 2245141 ● 复制保护 社会DRM

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