Classification of data streams by incremental semi-supervised fuzzy clustering

Abstract

Data stream mining refers to methods able to mine continuously arriving and evolving data sequences or even large scale static databases. Mining data streams has attracted much attention recently. Many data stream classification methods are supervised, hence they require labeled samples that are more difficult and expensive to obtain than unlabeled ones. This paper proposes an incremental semi-supervised clustering approach for data stream classification. Preliminary experimental results on the benchmark data set KDD-CUP’99 show the effectiveness of the proposed algorithm.