Employing nonlinear time series analysis tools with stable clustering algorithms for detecting concept drift on data streams

Employing nonlinear time series analysis tools with stable clustering algorithms for detecting concept drift on data streams

Several industrial, scientific and commercial processes produce open-ended sequences of observations which are referred to as data streams. We can understand the phenomena responsible for such streams by analyzing data in terms of their inherent recurrences and behavior changes. Recurrences supp...

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Bibliographic Details
Main Author: Fausto Guzzo da Costa
Other Authors: Rodrigo Fernandes de Mello
Language:English
Published: Universidade de São Paulo 2017
Subjects:
Agrupamento
Aprendizado de máquina
Fluxos de dados
Mudanças de conceito
Séries temporais não lineares
Clustering
Concept drift
Data streams
Machine learning
Nonlinear time series
Online Access:http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13112017-105506/

Internet

http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13112017-105506/

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