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)
We present an incremental learning method for document image and zone classification. We consider an industrial context where the system faces a large variability of digitized administrative documents that become available progressively over time. Each new incoming document is segmented into physical regions (zones) which are classified according to a(More)