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An Efficient Method to Detect Outliers in High Dimensional Data

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In this era, detection of outliers or anomalies from high dimensional data is really a great challenge. Normal data is distinguished from data containing anomalies using Outlier detection techniques which classifies new data as normal or abnormal. Different Outlier Detection algorithms are proposed by many researchers for high dimensional data and each algorithm has its own benefits and limitations. In the literature the researchers proposed different algorithms. For this work few algorithms such as Dice-Coefficient Index (DCI), Mapreduce Function and Linear Discriminant Analysis Algorithm (LDA) are considered. Mapreduce function is used to overcome the problem of large datasets. LDA is basically used in the reduction of the data dimensionality. In the present work a novel Hybrid Outlier Detection Algorithm (HbODA) is proposed for efficiently detection of outliers in high dimensional data. The important parameters efficiency, accuracy, computation cost, precision, recall etc. are focused for analyzing the performance of the novel hybrid algorithm. Experimental results on real large sets show that the proposed algorithm is better in detecting outliers than other traditional methods.
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Keywords: Dice Coefficient Index (DCI); Hybrid Outlier Detection Algorithm (HbODA); Linear Discriminant Analysis (LDA); Mapreduce Function; Outlier Detection

Document Type: Research Article

Affiliations: 1: Maharishi Markandeshswar Institute of Computer Technology & Business Management, Maharishi Markandeshswar (Deemed to be University), Mullana 133203, Haryana, India 2: Research Scholar, MMICT & BM, MM (Deemed to be University), Mullana 133203, Haryana, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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