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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Unsupervised density-based behavior change detection in data streams

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Autor(es):
Vallim, Rosane M. M. [1] ; Andrade Filho, Jose A. [1] ; de Mello, Rodrigo F. [1] ; de Carvalho, Andre C. P. L. F. [1] ; Gama, Joao [2]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Inst Math Sci & Computat, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Porto, LIADD INESC Porto, P-4100 Oporto - Portugal
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: Intelligent Data Analysis; v. 18, n. 2, p. 181-201, 2014.
Citações Web of Science: 3
Resumo

The ability to detect changes in the data distribution is an important issue in Data Stream mining. Detecting changes in data distribution allows the adaptation of a previously learned model to accommodate the most recent data and, therefore, improve its prediction capability. This paper proposes a framework for non-supervised automatic change detection in Data Streams called M-DBScan. This framework is composed of a density-based clustering step followed by a novelty detection procedure based on entropy level measures. This work uses two different types of entropy measures, where one considers the spatial distribution of data while the other models temporal relations between observations in the stream. The performance of the method is assessed in a set of experiments comparing M-DBScan with a proximity-based approach. Experimental results provide important insight on how to design change detection mechanisms for streams. (AU)

Processo FAPESP: 10/11250-0 - Mineração de Fluxos Contínuos de Dados para Jogos de Computador
Beneficiário:Rosane Maria Maffei Vallim
Modalidade de apoio: Bolsas no Brasil - Doutorado