Concept-evolution detection in non-stationary data streams: a fuzzy clustering approach

We have entered the era of networked communications where concepts such as big data and social networks are emerging. The explosion and profusion of available data in a broad range of application domains cause data streams to become an inevitable part of the most real-world applications. In the classification of data streams, there are four major challenges: infinite length, concept drift, recurring and evolving concepts. This paper proposes a novel method to address the mentioned challenges with a focus on the last one. Unlike the existing methods for detection of evolving concepts, we cast joint classification and detection of evolving concepts into optimizing an objective function by extending a fuzzy agglomerative clustering method. Moreover, rather than keeping instances or hyper-sphere summaries of previously seen classes, we just maintain boundaries in the kernel space and generate instances of each class on demand. This approach enhances the accuracy and reduces the memory usage of the proposed method. We empirically evaluated and showed the effectiveness of the proposed approach on several synthetic and real datasets. Experimental results on synthetic and real datasets show the superiority of the proposed method over the related state-of-the-art methods in this area.