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...
שמור ב:
| Auteurs principaux: | , , |
|---|---|
| פורמט: | Livre numérique |
| שפה: | Anglais |
| יצא לאור: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2009.
Cham : Springer Nature |
| סדרה: | Studies in Computational Intelligence
249 |
| גישה מקוונת: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| הערה: |
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 |
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| 009 | PPN139958614 | ||
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| 082 | |a 519 | ||
| 100 | 1 | |a Barbakh, Wesam Ashour. | |
| 245 | 1 | 0 | |a Non-Standard Parameter Adaptation for Exploratory Data Analysis |c by Wesam Ashour Barbakh, Ying Wu, Colin Fyfe. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2009. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 249 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a 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. | |
| 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. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets. We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods. We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation. | ||
| 700 | 1 | |a Wu, Ying, |c informaticien. |4 aut | |
| 700 | 1 | |a Fyfe, Colin. |4 aut | |
| 776 | 0 | |t Non-Standard Parameter Adaptation for Exploratory Data Analysis |b Texte imprimé |z 9783642040047 | |
| 776 | 0 | |t Non-Standard Parameter Adaptation for Exploratory Data Analysis |b Texte imprimé |z 9783642040375 | |
| 776 | 0 | |t Non-Standard Parameter Adaptation for Exploratory Data Analysis |b Texte imprimé |z 9783642260551 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/978-3-642-04005-4 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-G0BQPQMV-4 |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:747841039 |u https://ezproxy.univ-orleans.fr/login?url=https://dx.doi.org/10.1007/978-3-642-04005-4 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:750858567 |u https://ezproxy.insa-cvl.fr/login?qurl=https://dx.doi.org/10.1007/978-3-642-04005-4 |z Accès INSA CVL | |
| 997 | |0 942005 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||