Data Stream Unsupervised Partitioning Based on Optimized Fuzzy C-Means

Data stream partitioning is an important technique in data mining to analyze data streams in real-time. In this context, lots of data stream partitioning methods have been proposed. In the state of the art, most existing methods need to specify the number of classes before partitioning and/or introduce user-defined parameters for which the parameter values may differ for different data sets. In practice, it is difficult to determine the number of classes and the parameter values. Therefore, we propose in this paper an unsupervised and non-parametric method based on the Optimized Fuzzy C-Means algorithm. It has 2 steps. First is to partition a series of data chunks and then partition the intermediate classes formed before. The performance of the proposed algorithm is evaluated and compared with the recent state-of-the-art methods on hyperspectral image data sets.