Graph Clustering and Feature Selection for High Dimensional Data

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Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Big Data
Format: PDF
Feature selection techniques are used to select important items in the transactional data values. The features are used for the classification process. Clustering techniques are used for the feature selection process. Graph based clustering techniques are used to group up the transactional data with similarity values. Correlation similarity measures are used to identify the relevant and irrelevant features. Features And Subspace on Transactions (FAST) clustering-based feature selection algorithm is used to cluster the high dimensional data and feature selection process.
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