Gravitational approach to supervised clustering for bi-class datasets

There have been many researches about supervised clustering. The problem of common supervised clustering is to train a clustering algorithm by avoiding overfitting. To solve this problem, we develop a new algorithm based on gravitational cluster centers. The novel method avoids overfitting by taking account of the gradient between the misclassification error and the number of gravity centers. Also, it detects the number of gravity centers and their locations from the dataset. Two dimensional synthetic dataset are used in order to provide several viewpoints into this new method. Also, it is tested by using a benchmark datasets.