Survey on High Dimensional Data Clustering Using Fast Cluster Based Feature Selection

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Provided by: International Journal of Advance Research in Science and Engineering (IJARSE)
Topic: Data Management
Format: PDF
High-dimensional data often contain irrelevant or redundant features which slow down the mining process and cause difficulties in storage and retrieval. Feature selection is the process of selecting most relevant features from an entire set of features. The FAST (FAst clustering - based feature SelecTion algorithm) 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 subset from each cluster to form a subset of features.
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