Könyv Computational Intelligence in Data Mining Giacomo Della Riccia

Computational Intelligence in Data Mining

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
19 305 Ft
The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of cur...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2000
oldal
166
EAN
9783211833261
ISBN
3211833269
Enbook ID
06799051
Súly
318
Méretek
170 x 244 x 10

Teljes leírás

The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases the book starts with a unified view on 'Data Mining and Statistics A System Point of View'. Two special techniques follow: 'Subgroup Mining', and 'Data Mining with Possibilistic Graphical Models'. "Data Fusion and Possibilistic or Fuzzy Data Analysis is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models. The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases the book starts with a unified view on Data Mining and Statistics A System Point of View . Two special techniques follow: Subgroup Mining , and Data Mining with Possibilistic Graphical Models . "Data Fusion and Possibilistic or Fuzzy Data Analysis is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models.

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