OLINDDA: A cluster-based approach for detecting novelty and concept drift in data streams

• Type: Article in International Scientific Journal
• Date: 2007

Authors

Spinosa, EJ (Author) Other; De Carvalho, APDLF (Author) Other; João Gama (Author) FEP

Journal

Title: Proceedings of the ACM Symposium on Applied Computing

Pages: 448-452

Publisher: Association for Computing Machinery

Indexing

ISI Web of Knowledge - 71 Citations

Scopus - 103 Citations

Other information

Authenticus ID: P-007-JJA

DOI: 10.1145/1244002.1244107

Abstract (EN): A machine learning approach that is capable of treating data streams presents new challenges and enables the analysis of a variety of real problems in which concepts change over time. In this scenario, the ability to identify novel concepts as well as to deal with concept drift are two important attributes. This paper presents a technique based on the k-means clustering algorithm aimed at considering those two situations in a single learning strategy. Experimental results performed with data from various domains provide insight into how clustering algorithms can be used for the discovery of new concepts in streams of data. Copyright 2007 ACM.

Language: English

Type (Professor's evaluation): Scientific