Outlier Detection in Data Streams Using Fuzzy C-Mean Clustering, Outlier Detection and Genetic Algorithm

Authors: Pragya Giri, Abhishek Raghuvanshi

Published in: IJRRETAS, Volume 2, Issue 9, pp. 1-8 (01 Sep 2016)

Impact Factor: 2.3 (2016)

License: CC BY 4.0 CC BY 4.0 (Open Access)

Certificate: View Certificate

Abstract

Outliers with the rest of the data points which are different from or inconsistent. New novel, unusual, abnormal or may contain noise. Outliers are sometimes more interesting than the majority of the data. Increasing complexity, size and variety of datasets with major challenges outlier detection, a group, and how to evaluate similar as outliers outliers are to catch. This paper is an approach to detect outlier as a pre-processing step that uses semi surveillance describes outlier detection and then the fuzzy c-means clustering and genetic algorithm to cluster analysis dataset applies to analyze the effects of outliers. As data is digitized, connected and integrated systems, getting the scope of data and analyzes has been growing rapidly. Today, the system\'s most massive, the size, volume, speed of the phenomenon is changing rapidly, and the non-stationary data generated by these types of data are called data streams. Stream data and your issues in detail in this paper we explore the different techniques for nonreviewed and presented the same results. Keywords: Outlier Detection, Data Streams, Data Preprocessing, Fuzzy C-mean clustering and genetic algorithm.

Keywords
  • Outlier Detection
  • Data Streams
  • Data Preprocessing
  • Fuzzy C-mean clustering and genetic algorithm
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How to Cite
APA: Giri, P., & Raghuvanshi, A. (2016). Outlier Detection in Data Streams Using Fuzzy C-Mean Clustering, Outlier Detection and Genetic Algorithm. International Journal for Rapid Research in Engineering Technology & Applied Science, 2(9), 1–8. https://ijrretas.com/research-paper/outlier-detection-in-data-streams-using-fuzzy-cmean-clustering-outlier-detection-and-genetic-algorithm--240