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.

中文翻译：

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动态增量半监督模糊聚类的数据流分类

提出了一种基于增量半监督模糊聚类算法的数据流分类方法DISSFCM（Dynamic Incremental Semi-Supervised FCM）。该方法假设属于不同类别的部分标记数据在一段时间内以块的形式连续可用。每个块都通过半监督模糊聚类处理，形成基于聚类的分类模型。所提出的 DISSFCM 能够通过拆分低质量的集群来动态调整集群的数量以适应数据流，从而提高分类质量。合成数据和真实世界数据的实验结果表明了所提出的方法在数据流分类中的有效性。