Mining Complex Data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2009
|
| Schriftenreihe: | Studies in Computational Intelligence
165 |
| Schlagworte: | |
| Online-Zugang: | BTU01 FHN01 FHR01 Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9783540880677 |
| DOI: | 10.1007/978-3-540-88067-7 |
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Datensatz im Suchindex
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| author | Zighed, Djamel A. |
| author_facet | Zighed, Djamel A. |
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| contents | 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 |
| ctrlnum | (OCoLC)612122189 (DE-599)BVBBV041889663 |
| dewey-full | 519 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519 |
| dewey-search | 519 |
| dewey-sort | 3519 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik |
| doi_str_mv | 10.1007/978-3-540-88067-7 |
| format | Electronic eBook |
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| spelling | Zighed, Djamel A. Verfasser aut Mining Complex Data edited by Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras, Hakim Hacid Berlin, Heidelberg Springer Berlin Heidelberg 2009 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Studies in Computational Intelligence 165 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 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 Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf 1\p (DE-588)4143413-4 Aufsatzsammlung gnd-content Data Mining (DE-588)4428654-5 s 2\p DE-604 Wissensextraktion (DE-588)4546354-2 s 3\p DE-604 Tsumoto, Shusaku Sonstige oth Ras, Zbigniew W. Sonstige oth Hacid, Hakim Sonstige oth Erscheint auch als Druckausgabe 978-3-540-88066-0 https://doi.org/10.1007/978-3-540-88067-7 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Zighed, Djamel A. Mining Complex Data 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 Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Wissensextraktion (DE-588)4546354-2 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4546354-2 (DE-588)4428654-5 (DE-588)4143413-4 |
| title | Mining Complex Data |
| title_auth | Mining Complex Data |
| title_exact_search | Mining Complex Data |
| title_full | Mining Complex Data edited by Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras, Hakim Hacid |
| title_fullStr | Mining Complex Data edited by Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras, Hakim Hacid |
| title_full_unstemmed | Mining Complex Data edited by Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras, Hakim Hacid |
| title_short | Mining Complex Data |
| title_sort | mining complex data |
| topic | Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Wissensextraktion (DE-588)4546354-2 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Engineering Artificial intelligence Engineering mathematics Appl.Mathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Ingenieurwissenschaften Künstliche Intelligenz Wissensextraktion Data Mining Aufsatzsammlung |
| url | https://doi.org/10.1007/978-3-540-88067-7 |
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