Design Of Fast Clustering Based Feature Subset Selection Algorithm For High Dimensional Data

Mr Sathish, Mrs Hemalatha
2015 Special Issue Published in Int. Jnl. Of Advanced Networking and Applications   unpublished
A Feature selection algorithm is employed for removing irrelevant, redundant information from the data set. Amongst feature subset selection algorithm, filter methods are used because of its generality and are usually good choice when number of features are large. A Fast clustering based feature selection algorithm is based on MST method. In the FAST algorithm, features are divided into clusters by using graph-theoretic clustering method. A feature subset selection algorithm (FAST) is used to
more » ... st high dimensional available image, microarray, and text data sets. Traditionally, feature subset selection research has focused on searching for relevant features. The clustering based strategy of FAST has a high probability of producing a subset of useful and independent features. In the proposed algorithm, removing of redundant data in the dataset is considered to reduce time complexity and improving learning accuracy.
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