Most existing clustering approaches not only require several scans of a dataset but also have a very high computational cost. In this paper, we propose a novel, efficient, and effective clustering framework which requires only one scan of the input dataset. In the beginning, the original dataset is transformed and merged into a hyper-image. After that, the dissimilarities between data points ar...

Streaming data, consisting of indefinitely evolving sequences, are becoming ubiquitous in many branches of science and in various applications. Computer scientists have developed streaming applications such as Storm and the S4 distributed stream computing platform1 to deal with data streams. However, in current production packages testing and evaluating streaming algorithms is cumbersome. This ...

In order to tackle high dimensional, complex problems, learning models have go deeper. this article, a novel multilayer ensemble model with first-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on sample-by-sample basis and self-organizes multilayered system structure meta-parameters in feedforward, noniterative ...