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Adaptive Resonance Theory (ART) is an unsupervised neural network. Fuzzy ART is a variation of ART, allows both binary and analogue input patterns. However, Fuzzy ART has the cluster overlapping problem. In this study, to solve this problem, we propose a new Improved Fuzzy ART (IFART) algorithm. In the proposed algorithm, after the clusters are formed, membership degrees of each data instance to all clusters are calculated according to the cluster centers. If data instances are not in the cluster with maximum membership degree, then they are moved between clusters according to their maximum membership degrees. The clustering results on real sample datasets are investigated and compared with the conventional Fuzzy ART. It is seen that, Improved Fuzzy ART is more efficient then Fuzzy ART and also a high performance algorithm than SOM.

The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions. Within the scope of this study, a new method based on the Fuzzy ART algorithm which is called Improved Fuzzy ART (IFART) is used in the determination of initial cluster centers. By using IFART, better quality clusters are achieved than Fuzzy ART do and also IFART is as good as Fuzzy ART about capable of fast clustering and capability on large scaled data clustering. Consequently, it is observed that, with the proposed method, the clustering operation is completed in fewer steps, that it is performed in a more stable manner by fixing the initialization points and that it is completed with a smaller error margin compared with the conventional K-means.

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