A Modified Hybrid Fuzzy Clustering Algorithm for Data Partitions

The clustering algorithm hybridization scheme has become of research interest in data partitioning applications in recent years. The present paper proposes a Hybrid Fuzzy clustering algorithm (combination of Fuzzy C-means with extension and Subtractive clustering algorithm) for data classifications applications. The fuzzy c-means (FCM) and subtractive clustering (SC) algorithm has been widely discussed and applied in pattern recognitions, machine learning and data classifications. However the FCM could not guarantee unique clustering result because initial cluster number is chosen randomly as the result of the classification is unstable. On the other hand, the SC is a fast, one-pass algorithm for estimating the numbers and center of clusters for a set of data. This paper presents the two different clustering algorithms and their comparison. First clustering algorithm is fuzzy c-means clustering, and second is subtractive clustering. Results show that the SC is better than FCM in respect of speed but not as good in accuracy, so a modified hybrid clustering algorithm is designed with all these parameters. The experiments show that the hybrid clustering algorithm can improve the speed, and reduce the iterative amount. At the same time, the hybrid algorithm can make the results of data partitions are more stable and higher accuracy.