Data mining is the set of computational techniques and methodologies aimed to extract knowledge from a large amount of data, by using sophisticated data analysis tools to highlight information structure underlying large data sets. Machine learning methods represent one of these tools, allowing, not only data management but also analysis and prediction operations. Supervised learning, a kind of machine learning methodology, uses input data and products outputs of two type: qualitative and quantitative, respectively describing data classes and predicting data trends. Classification task provides qualitative responses whereas prediction or regression task offers quantitative outputs

Data Mining: Classification and Prediction

A Urso;A Fiannaca;M La Rosa;R Rizzo
2019

Abstract

Data mining is the set of computational techniques and methodologies aimed to extract knowledge from a large amount of data, by using sophisticated data analysis tools to highlight information structure underlying large data sets. Machine learning methods represent one of these tools, allowing, not only data management but also analysis and prediction operations. Supervised learning, a kind of machine learning methodology, uses input data and products outputs of two type: qualitative and quantitative, respectively describing data classes and predicting data trends. Classification task provides qualitative responses whereas prediction or regression task offers quantitative outputs
2019
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Inglese
Guenther, R.; Steel, D.
Encyclopedia of Bioinformatics and Computational Biology
384
402
18
978-0-12-811432-2
Elsevier
Oxford
REGNO UNITO DI GRAN BRETAGNA
Sì, ma tipo non specificato
Associative classification
Classification
Decision tree induction
Multilayer feed-forward neural network
Prediction
Rule-based classification
Support vector machines
5
02 Contributo in Volume::02.01 Contributo in volume (Capitolo o Saggio)
268
none
Urso, A; Fiannaca, A; LA ROSA, Massimo; Ravì, V; Rizzo, R
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/372326
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