Integrating type-1 fuzzy and type-2 fuzzy clustering with k-means for pre-processing input data in classification algorithms

In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.