Detecting Outliers in High Dimensional Data Sets Using Z-Score Methodology
Peruri VenkataAnusha1, Ch.Anuradha2, Patnala S.R. Chandra Murty3, Ch. Surya Kiran4
1PeruriVenkataanusha*, Research Schholar, Dept of CSE, Acharya Nagarjuna University, Nagarjuna Nagar, India.
2Ch.Anuradha, Assistant professor, Dept of CSE, VRSEC, Vijayawada
3Dr. Patnala S.R. Chandra Murty, Research Supervisor, Dept of CSE, Acharya Nagarajuna University.
4Dr. Surya Kian Chebrolu, Associate Professor, Dept of CSE, NRI Institute of Technology
Manuscript received on October 15, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 48-53 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3910119119/2019©BEIESP | DOI: 10.35940/ijitee.A3910.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Outlier detection is an interesting research area in machine learning. With the recently emergent tools and varied applications, the attention of outlier recognition is growing significantly. Recently, a significant number of outlier detection approaches have been observed and effectively applied in a wide range of fields, comprising medical health, credit card fraud and intrusion detection. They can be utilized for conservative data analysis. However, Outlier recognition aims to discover sequence in data that do not conform to estimated performance. In this paper, we presented a statistical approach called Z-score method for outlier recognition in high-dimensional data. Z-scores is a novel method for deciding distant data based on data positions on charts. The projected method is computationally fast and robust to outliers’ recognition. A comparative Analysis with extant methods is implemented with high dimensional datasets. Exploratory outcomes determines an enhanced accomplishment, efficiency and effectiveness of our projected methods. Keywords: Outliers, Ionosphere, Z-score Method, Clusters, High Dimensional Data.
Keywords: Outliers, Ionosphere, Z-score Method, Clusters, High Dimensional Data.
Scope of the Article: Cloud, Cluster, Grid and P2P Computing