A FAST Algorithm for High Dimensional Data using Clustering-Based Feature Subset Selection
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 11
Abstract
Feature subset clustering is a powerful technique to reduce the dimensionality of feature vectors for text classification and involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A novel approach called supervised attribute clustering algorithm is proposed to improve the accuracy and check the probability of the patterns. 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. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. Efficiency is related to the time required to find a subset of features while the effectiveness is related to quality of subset of features.Features in different clusters are relatively independent; the clusteringbased 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 clustering method.
Authors and Affiliations
Puppala Priyanka, M Swapna
Keywords
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