A density-based clustering approach for behavior change detection in data streams

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Rosane Mafei (Author) Other; Andre Carvalho (Author) Other; José Filho (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: Proceedings - Brazilian Symposium on Neural Networks, SBRN

Pages: 37-42

2012 Brazilian Conference on Neural Networks, SBRN 2012

Curitiba, Parana, 20 October 2012 through 25 October 2012

Indexing

Scopus - 4 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-008-918

DOI: 10.1109/sbrn.2012.22

Abstract (EN): Mining data streams poses many challenges to existing Machine Learning algorithms. Algorithms designed to learn in this scenario need to constantly update their decision models in accordance with current data behavior. Therefore, the ability to detect when the behavior of the stream is changing is an important feature of any learning technique approaching data streams. This work is concerned with unsupervised behavior change detection. It suggests the use of density-based clustering and an entropy measurement for change detection that is independent of the number and format of clusters. The proposed approach uses a modified version of the Den Stream algorithm that is designed to better cope with the entropy calculation. Experimental results using synthetic data provide insight on how clustering and novelty detection algorithms can be used for change detection in data streams. © 2012 IEEE.

Language: English

Type (Professor's evaluation): Scientific