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 w...
Guardado en:
| Autor principal: | |
|---|---|
| Otros Autores: | , , , , , , , |
| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edición: | 1st ed. 2009. |
| Colección: | Studies in Computational Intelligence
165 |
| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Mining complex data, Djamel A. Zighed et al. (ed.), Berlin, Springer, 2009, 1 vol. (XII-300 p.), Studies in computational intelligence, 978-3-540-88066-0 • Mining Complex Data, Texte imprimé, 9783642099809 • Mining Complex Data, Texte imprimé, 9783540881704 • Mining complex data, Djamel A. Zighed et al. (ed.), Berlin, Springer, 2009, 1 vol. (XII-300 p.), Studies in computational intelligence, 978-3-540-88066-0 |
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| 100 | 1 | |a Zighed, Djamel A. | |
| 245 | 1 | 0 | |a Mining complex data |c edited by Janusz Kacprzyk, Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras, Hakim Hacid. |
| 250 | |a 1st ed. 2009. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 165 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
| 700 | 1 | |a Tsumoto, Shusaku. |4 edt | |
| 700 | 1 | |a Ras, Zbigniew W. |4 edt | |
| 700 | 1 | |a Hacid, Hakim. |4 edt | |
| 700 | 1 | |a Kacprzyk, Janusz, |d 1947- |4 pbd | |
| 700 | 1 | |a Hacid, Hakim, |d 1979- |4 pbd | |
| 700 | 1 | |a Ras, Zbigniew W., |d 1947- |4 pbd | |
| 700 | 1 | |a Tsumoto, Shusaku, |d 1963- |4 pbd | |
| 700 | 1 | |a Zighed, Djamel Abdelkader, |d 1955- |4 pbd | |
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| 776 | 0 | |t Mining Complex Data |b Texte imprimé |z 9783642099809 | |
| 776 | 0 | |t Mining Complex Data |b Texte imprimé |z 9783540881704 | |
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