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Published in

World Scientific Publishing, International Journal on Artificial Intelligence Tools, 08(28), p. 1960009, 2019

DOI: 10.1142/s0218213019600091

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Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering

Journal article published in 2019 by Gabriella Casalino ORCID, Giovanna Castellano ORCID, Corrado Mencar
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based classification model. The proposed DISSFCM is capable of dynamically adapting the number of clusters to data streams, by splitting low-quality clusters so as to improve classification quality. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method in data stream classification.