Mohamed-Rafik Bouguelia

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Usually, incremental algorithms for data streams clustering not only suffer from sensitive initialization parameters , but also incorrectly represent large classes by many cluster representatives, which leads to decrease the computational efficiency over time. We propose in this paper an incremental clustering algorithm based on " growing neural gas "(More)
—We consider an industrial context where we deal with a stream of unlabelled documents that become available progressively over time. Based on an adaptive incremental neural gas algorithm (AING), we propose a new stream-based semi-supervised active learning method (A2ING) for document classification , which is able to actively query (from a human annotator)(More)
—In the push-pull-clone collaborative editing model widely used in distributed version control systems users replicate shared data, modify it and redistribute modified versions of this data without the need of a central authority. However, in this model no usage restriction mechanism is proposed to control what users can do with the data after it has been(More)
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