An Empirical Model of Clustering Algorithm for High Dimensional Data
Feature set extraction from raw dataset is always an interesting and important research issue, here useful features extracted from set of features of dataset. In this paper, the authors are proposing an efficient clustering algorithm i.e. fast clustering-based feature selection. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features.