Correlation Based Feature Selection with Clustering for High Dimensional Data - Engineering Assignment Help

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Abstract 
Feature selection is an essential technique to reduce the dimensionality problem in data mining task. Traditional feature selection algorithms are fail to scale on large space. This paper proposes a new method to solve dimensionality problem where clustering is integrating with correlation measure to produce good feature subset. First Irrelevant features are eliminated by using k-means clustering method and then non-redundant features are selected by correlation measure from each cluster. The proposed method is evaluate on Microarray and Text datasets and the results are compared with other renowned feature selection methods using Naïve Bayes classifier. To verify the accuracy of the proposed method with different number of relevant features, percentagewise criteria is used. The experimental results reveal the efficiency and accuracy of the proposed method. © 2018 Electronics Research Institute (ERI). Production and hosting by Elsevier B.V. This is an open access article under the CC  

1. Introduction 
Most of applications such as genes data, text categorization, image retrieval and information retrieval contain vast amounts of multivariate data in terms of instances and attributes. This large data volume far outpaces human’s ability to understand and handle it. Data mining task plays an important role to discover patterns in such large volume of data. It is challenging for machine learning to find relevant and non-redundant data from the applications, which contains hundreds to thousands of attributes, these higher, and more multifaceted data accumulate at an unprecedented speed. While performing operation and representing data large number of features are collected because of unfamiliar 
 

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