Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering
This addresses the challenge of adapting classifiers to time-varying data streams, which is incremental as it builds on existing fuzzy clustering with new granular structures.
The authors tackled the problem of classifying data from nonstationary online streams by introducing Fuzzy eIX, an unsupervised evolving fuzzy clustering method that maintains accuracy where offline-trained classifiers fail, as demonstrated on the Rotation of Twin Gaussians synthetic problem.
Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.