An Unsupervised Approach For Selection of Candidate Feature Set Using Filter Based Techniques

Year 2018, Volume: 31 Issue: 3, 789 - 799, 01.09.2018

Sai Prasad Potharaju

Abstract

Clustering
is an unsupervised Data Mining approach.In this research article, we
have proposed an unsupervised approach using filter based feature
selection methods and K-Means clustering technique to derive the
candidate subset. Initially, score of each feature is recorded using
traditional filter based methods, then normalized the dataset using
Min-Max technique, then formed the unsupervised dataset. K-Means
algorithm is employed on the dataset to form the clusters of
features. To decide the strong subset, Multi Layer Perceptron(MLP) is
applied on each cluster. Based on the minimum Root Mean Square (RMS)
error rate given by MLP best cluster is selected. This framework is
compared with traditional methods over six well known datasets having
the total features in between 34 and 90 using various classification
algorithms. The proposed method has shown competitive performance
than few of the traditional methods.

Keywords

Feature Selection, Clustering, Multi Layer Perceptron, Min-Max Normalization, Filter Methods

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