ULODF: An Unsupervised Learning based Outlier Detection Framework in High Dimensional Data


  • C. Jayaramulu, Bondu Venkateswarlu


Outlier Detection, Unsupervised Learning, Outlier Detection Framework, Clustering High Dimensional Data


Outliers play crucial role in applications like disease diagnosis, fraud detection techniques and cyber security to mention few. Unsupervised learning techniques like clustering are widely used, in the area of machine learning, towards outlier detection. However, most of the existing methods did not consider dual tasking benefits of using clustering that not only renders quality clusters but also identifies outliers effectively. We proposed a framework named Unsupervised Learning based Outlier Detection Framework (UL-ODF). An algorithm named Novel Outlier Detection Method in High Dimensional Data (NODM-HDD) is defined. The algorithm has mechanisms to improve compactness of clusters made besides determining outliers. The algorithm exploits an enhanced version of K-Means clustering technique. A prototype is built to validate the utility of the framework and the underlying algorithm. Different benchmark datasets and metrics are used in the empirical study. The experimental results revealed that the NODM-HDD shows better performance over the state of the art.


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How to Cite

Bondu Venkateswarlu, C. J. . (2024). ULODF: An Unsupervised Learning based Outlier Detection Framework in High Dimensional Data. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1675–1686. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5643



Research Article