Clustering High Dimensional Data Using Fast Algorithm

Abstract

Feature selection reduces the computational time greatly due to reduced feature subset and also improves clustering quality. Based on these criteria, a fast clustering-based feature selection algorithm (FAST) is used and experimentally evaluated in this project. The FAST algorithm works in two steps. In the first step, features are divided into clusters. 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. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) using the Kruskal’s Algorithm clustering method. The efficiency and effectiveness of the FAST algorithm is evaluated through the study.