Survey on Fast Clustering-Based Feature Subset Selection Algorithm for High Dimensional Data

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Provided by: Creative Commons
Topic: Data Management
Format: PDF
Feature selection involves 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. The FAST algorithm works in two steps: features are divided into clusters by using graph-theoretic clustering methods, and the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features.
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