Evolving Fuzzy Clustering Approach: An Epoch Clustering That Enables Heuristic Postpruning

Clustering is an unsupervised machine learning method that is used both individually, and as a part of the pre-processing stage for supervised machine learning methods. Due to its unsupervised nature, clustering results have less accuracy compared to supervised learning. This study aims to introduce a new perspective in clustering by defining an approach for data pruning. The method also enables clustering using multiple sets of prototypes instead of only one set to improve clustering accuracy. Consequently, this approach has the potential to be used independently or as a part of a pre-processing to prepare purified data for the training step of a supervised learning approach. EFCA utilizes the fuzzy membership concept to breakdown clustering in epochs instead of running the clustering on all data at once. In some cases, for supervised learning, we rather have a smaller subset of highly accurate labeled data instead of a dataset with less accurate labels. EFCA's „epoch cut‟ enables post pruning ability to eliminate obscure data points which result in more clustering accuracy. EFCA has been applied to a set of 8 multivariate and ten time-series datasets, and for example, after deploying epoch cut and eliminating obscure data (20% of data) by automatic post pruning it achieved 100 percent accuracy for the rest 80% Iris data.