Unsupervised Change Detection in Data Streams

• Type: Article in International Scientific Journal
• Date: 2014

Authors

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

Journal

Title: Intelligent Data Analysis

ISSN: 1088-467X

Publisher: IOS PRESS

Indexing

ISI Web of Science - Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

DOI: 10.3233/IDA-140636

Resumo (PT): 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. [ABSTRACT FROM AUTHOR] Copyright of Intelligent Data Analysis is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Language: English

Type (Professor's evaluation): Scientific