Mining Complex Data [electronic resource] /
Zighed, Djamel A.editor.
Tsumoto, Shusaku.editor.
Ras, Zbigniew W.editor.
Hacid, Hakim.editor.
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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
General Aspects of Complex Data -- Using Layout Data for the Analysis of Scientific Literature -- Extracting a Fuzzy System by Using Genetic Algorithms for Imbalanced Datasets Classification: Application on Down's Syndrome Detection -- A Hybrid Approach of Boosting Against Noisy Data -- Dealing with Missing Values in a Probabilistic Decision Tree during Classification -- Kernel-Based Algorithms and Visualization for Interval Data Mining -- Rules Extraction -- Evaluating Learning Algorithms Composed by a Constructive Meta-learning Scheme for a Rule Evaluation Support Method -- Mining Statistical Association Rules to Select the Most Relevant Medical Image Features -- From Sequence Mining to Multidimensional Sequence Mining -- Tree-Based Algorithms for Action Rules Discovery -- Graph Data Mining -- Indexing Structure for Graph-Structured Data -- Full Perfect Extension Pruning for Frequent Subgraph Mining -- Parallel Algorithm for Enumerating Maximal Cliques in Complex Network -- Community Finding of Scale-Free Network: Algorithm and Evaluation Criterion -- The k-Dense Method to Extract Communities from Complex Networks -- Data Clustering -- Efficient Clustering for Orders -- Exploring Validity Indices for Clustering Textual 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.
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Mathematical and Computational Engineering.
Artificial Intelligence.
Studies in Computational Intelligence