Ebook: Mining Complex Data

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.

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The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.

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The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Using Layout Data for the Analysis of Scientific Literature....Pages 3-22
Extracting a Fuzzy System by Using Genetic Algorithms for Imbalanced Datasets Classification: Application on Down’s Syndrome Detection....Pages 23-39
A Hybrid Approach of Boosting Against Noisy Data....Pages 41-54
Dealing with Missing Values in a Probabilistic Decision Tree during Classification....Pages 55-74
Kernel-Based Algorithms and Visualization for Interval Data Mining....Pages 75-91
Front Matter....Pages 93-93
Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method....Pages 95-111
Mining Statistical Association Rules to Select the Most Relevant Medical Image Features....Pages 113-131
From Sequence Mining to Multidimensional Sequence Mining....Pages 133-152
Tree-Based Algorithms for Action Rules Discovery....Pages 153-163
Front Matter....Pages 165-165
Indexing Structure for Graph-Structured Data....Pages 167-188
Full Perfect Extension Pruning for Frequent Subgraph Mining....Pages 189-205
Parallel Algorithm for Enumerating Maximal Cliques in Complex Network....Pages 207-221
Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion....Pages 223-242
The k-Dense Method to Extract Communities from Complex Networks....Pages 243-257
Front Matter....Pages 259-259
Efficient Clustering for Orders....Pages 261-279
Exploring Validity Indices for Clustering Textual Data....Pages 281-300
Back Matter....Pages -

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The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006.

The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Using Layout Data for the Analysis of Scientific Literature....Pages 3-22
Extracting a Fuzzy System by Using Genetic Algorithms for Imbalanced Datasets Classification: Application on Down’s Syndrome Detection....Pages 23-39
A Hybrid Approach of Boosting Against Noisy Data....Pages 41-54
Dealing with Missing Values in a Probabilistic Decision Tree during Classification....Pages 55-74
Kernel-Based Algorithms and Visualization for Interval Data Mining....Pages 75-91
Front Matter....Pages 93-93
Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method....Pages 95-111
Mining Statistical Association Rules to Select the Most Relevant Medical Image Features....Pages 113-131
From Sequence Mining to Multidimensional Sequence Mining....Pages 133-152
Tree-Based Algorithms for Action Rules Discovery....Pages 153-163
Front Matter....Pages 165-165
Indexing Structure for Graph-Structured Data....Pages 167-188
Full Perfect Extension Pruning for Frequent Subgraph Mining....Pages 189-205
Parallel Algorithm for Enumerating Maximal Cliques in Complex Network....Pages 207-221
Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion....Pages 223-242
The k-Dense Method to Extract Communities from Complex Networks....Pages 243-257
Front Matter....Pages 259-259
Efficient Clustering for Orders....Pages 261-279
Exploring Validity Indices for Clustering Textual Data....Pages 281-300
Back Matter....Pages -
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