alexa Unsupervised Distance-Based Outlier Detection Using Nearest Neighbours Algorithm on Distributed Approach: Survey
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
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Research Article

Unsupervised Distance-Based Outlier Detection Using Nearest Neighbours Algorithm on Distributed Approach: Survey

Jayshree S.Gosavi1, Vinod S.Wadne2
  1. PG Student, Department of Computer Engineering, ICOER, Savitribai Phule Pune University, Wagholi, Pune, India
  2. Assistant Professor, Department of Computer Engineering, ICOER, Savitribai Phule Pune University, Wagholi, Pune, India
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Abstract

Outlier detection is the process of finding outlying pattern from a given dataset. Outlier detection became important subject in different knowledge domains. Data size is getting doubled every years there is a need to detect outliers in large datasets as early as possible. In high-dimensional data outlier detection presents various challenges because of curse of dimensionality. By examining again the notion of reverse nearest neighbors in the unsupervised outlier-detection context, high dimensionality can have a different impact. In high dimensions it was observed that the distribution of points in reverse-neighbor counts becomes skewed .This proposed work aims at developing and comparing some of the unsupervised outlier detection methods and propose a way to improve them. This proposed work goes in details about the development and analysis of outlier detection algorithms such as Local Outlier Factor(LOF), Local Distance-Based Outlier Factor(LDOF) , Influenced Outliers and .The concepts of these methods are then combined to implement a new method with distributed approach which improves the results of the previous mentioned ones with reference to speed, complexity and accuracy.

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