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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2007

Autores

Spinosa, EJ (Autor) Outra; De Carvalho, APDLF (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Proceedings of the ACM Symposium on Applied Computing

Páginas: 448-452

Editora: Association for Computing Machinery

Indexação

ISI Web of Knowledge - 65 Citações

Scopus - 93 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica