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Mining Complex Data

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Cover of 'Mining Complex Data'

Table of Contents

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    Book Overview
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    Chapter 1 Using Layout Data for the Analysis of Scientific Literature
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    Chapter 2 Extracting a Fuzzy System by Using Genetic Algorithms for Imbalanced Datasets Classification: Application on Down’s Syndrome Detection
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    Chapter 3 A Hybrid Approach of Boosting Against Noisy Data
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    Chapter 4 Mining Complex Data
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    Chapter 5 Kernel-Based Algorithms and Visualization for Interval Data Mining
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    Chapter 6 Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method
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    Chapter 7 Mining Statistical Association Rules to Select the Most Relevant Medical Image Features
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    Chapter 8 From Sequence Mining to Multidimensional Sequence Mining
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    Chapter 9 Tree-Based Algorithms for Action Rules Discovery
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    Chapter 10 Indexing Structure for Graph-Structured Data
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    Chapter 11 Full Perfect Extension Pruning for Frequent Subgraph Mining
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    Chapter 12 Parallel Algorithm for Enumerating Maximal Cliques in Complex Network
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    Chapter 13 Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion
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    Chapter 14 The k -Dense Method to Extract Communities from Complex Networks
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    Chapter 15 Efficient Clustering for Orders
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    Chapter 16 Exploring Validity Indices for Clustering Textual Data
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Title
Mining Complex Data
Published by
Springer Berlin Heidelberg, October 2008
DOI 10.1007/978-3-540-88067-7
ISBNs
978-3-54-088066-0, 978-3-54-088067-7
Editors

Zighed, Djamel A., Tsumoto, Shusaku, Ras, Zbigniew W., Hacid, Hakim

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