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

Authors:
Eduardo J. Spinosa;André Ponce de Leon F. de Carvalho;João Gama
Affiliations:
University of Sao Paulo (USP), Sao Carlos, SP, Brazil;University of Sao Paulo (USP), Sao Carlos, SP, Brazil;University of Porto (UP), Porto, Portugal
Venue:
Proceedings of the 2007 ACM symposium on Applied computing
Year:
2007

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Abstract

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.