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Outlier Detection Using Unsupervised Learning on High Dimensional Data

By Sunil Bhutada and Anusha Velicheti2

Abstract

The outliers in data mining can be detected using semi-supervised and unsupervised methods. Outlier\ud detection in high dimensional data faces various challenges from curse of dimensionality. It means due\ud to the distance concentration the data becomes unobvious in high dimensional data. Using outlier\ud detection techniques, the distance base methods are used to detect outliers and label all the points as\ud good outliers. In high dimensional data to detect outliers effectively, we use unsupervised learning\ud methods like IQR, KNN with Anti hub

Topics: Outlier detection, unsupervised and semi-supervised learning, high dimensional data., Engineering (General). Civil engineering (General), TA1-2040, Technology, T
Publisher: International Journal of Engineering Research and Applications
Year: 2016
OAI identifier: oai:doaj.org/article:5e7096a5a909402593dd4921b6e4dd86
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