Outlier detection in spatial data using the m-SNN algorithm, 2013

Collection:
Atlanta University and Clark Atlanta University Theses and Dissertations
Title:
Outlier detection in spatial data using the m-SNN algorithm, 2013
Creator:
Parana-Liyanage, Krishani
Date of Original:
2010/2019
Subject:
Degrees, Academic
Dissertations, Academic
Location:
United States, Georgia, Fulton County, Atlanta, 33.749, -84.38798
Medium:
theses
dissertations
Type:
Text
Format:
application/pdf
Description:
Outlier detection is an important topic in data analysis because of its applications to numerous domains. Its application to spatial data, and in particular spatial distribution in path distributions, has recently attracted much interest. This recent trend can be seen as a reflection of the massive amounts of spatial data being gathered through mobile devices, sensors and social networks. In this thesis we propose a nearest neighbor distance based method the Modified-Shared Nearest Neighbor outlier detection (m-SNN) developed for outlier detection in spatial domains. We modify the SNN technique for use in outlier detection, and compare our approach with the widely used outlier detection technique, the LOF Algorithm and a base Gaussian approach. It is seen that the m-SNN compares well with the LOF in simple spatial data distributions and outperforms it in more complex distributions. Experimental results of using buoy data to track the path of a hurricane are also shown.
Date of award: 7/1/2013
Degree type: thesis
Degree name: Master of Science (MS)
Granting institution: Clark Atlanta University
Department: School of Arts and Sciences, Computer and Information Sciences
Advisor: George, Roy
Metadata URL:
http://hdl.handle.net/20.500.12322/cau.td:2013_parana_liyanage_krishani
Holding Institution:
Atlanta University Center Robert W. Woodruff Library
Rights:
Rights Statement information

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