High Dimensional Data Clustering Based On Feature Selection Algorithm
Main Article Content
Abstract
Feature selection is the process of identifying a subset of the most useful features that produces
compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the
efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of
features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a FAST
clustering-based feature Selection algorithm (FAST) is proposed and experimentally evaluated. 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. 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. The MinimumSpanning Tree (MST) using Prim’s algorithm can concentrate on one tree at a time. To ensure the efficiency of FAST,
adopt the efficient MST using the Kruskal’s Algorithm clustering method.
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