Non-Standard Parameter Adaptation for Exploratory Data Analysis
Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by revie...
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| Hlavní autoři: | , , |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2009.
Cham : Springer Nature |
| Edice: | Studies in Computational Intelligence
249 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Non-Standard Parameter Adaptation for Exploratory Data Analysis, Texte imprimé, 9783642040047 • Non-Standard Parameter Adaptation for Exploratory Data Analysis, Texte imprimé, 9783642040375 • Non-Standard Parameter Adaptation for Exploratory Data Analysis, Texte imprimé, 9783642260551 |
Obsah:
- Review of Clustering Algorithms
- Review of Linear Projection Methods
- Non-standard Clustering Criteria
- Topographic Mappings and Kernel Clustering
- Online Clustering Algorithms and Reinforcement Learning
- Connectivity Graphs and Clustering with Similarity Functions
- Reinforcement Learning of Projections
- Cross Entropy Methods
- Artificial Immune Systems
- Conclusions.