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Multivariate outlier mining using cl...

Sharker, Md Monir Hossain.

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Multivariate outlier mining using cluster analysis: Case study - National Health Interview Survey.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multivariate outlier mining using cluster analysis: Case study - National Health Interview Survey./
Author:	Sharker, Md Monir Hossain.
Description:	71 p.
Notes:	Source: Masters Abstracts International, Volume: 49-02, page: 1232.
Contained By:	Masters Abstracts International49-02.
Subject:	Applied Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1482750
ISBN:	9781124303093

Multivariate outlier mining using cluster analysis: Case study - National Health Interview Survey.
Sharker, Md Monir Hossain.

Multivariate outlier mining using cluster analysis: Case study - National Health Interview Survey. - 71 p.

Source: Masters Abstracts International, Volume: 49-02, page: 1232.

Thesis (M.S.)--Duquesne University, 2010.

Outlier mining is a fundamental issue in many statistical analyses, especially in multivariate cases. Outliers may exert undue influence on outcomes of the analysis. In most cases, it is a big challenge to reveal the pattern of the outliers and the "outlyingness". There are several approaches and methods to detect anomalous data points in data. But no single method is perfect for every data set especially when the data dimension and volume is high. In this thesis, I review distance-based clustering methods for multivariate outlier mining and demonstrate the usefulness of it in a medical setting. Specifically, I discuss Hierarchical clustering and the multivariate methods of determining appropriate cluster(s). After mining the multivariate outliers, I examine and describe the characteristics of the variables for those outliers. Finally, I demonstrate the application of these methods using the National Health Interview Survey (NHIS) 2008 database for the purposes of studying adolescent obesity.

ISBN: 9781124303093Subjects--Topical Terms:

1669109
Applied Mathematics.

Multivariate outlier mining using cluster analysis: Case study - National Health Interview Survey.
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020 $a 9781124303093
035 $a (UMI)AAI1482750
035 $a AAI1482750
040 $a UMI $c UMI
100 1 $a Sharker, Md Monir Hossain. $3 1681765
245 1 0 $a Multivariate outlier mining using cluster analysis: Case study - National Health Interview Survey.
300 $a 71 p.
500 $a Source: Masters Abstracts International, Volume: 49-02, page: 1232.
500 $a Adviser: Frank D'Amico.
502 $a Thesis (M.S.)--Duquesne University, 2010.
520 $a Outlier mining is a fundamental issue in many statistical analyses, especially in multivariate cases. Outliers may exert undue influence on outcomes of the analysis. In most cases, it is a big challenge to reveal the pattern of the outliers and the "outlyingness". There are several approaches and methods to detect anomalous data points in data. But no single method is perfect for every data set especially when the data dimension and volume is high. In this thesis, I review distance-based clustering methods for multivariate outlier mining and demonstrate the usefulness of it in a medical setting. Specifically, I discuss Hierarchical clustering and the multivariate methods of determining appropriate cluster(s). After mining the multivariate outliers, I examine and describe the characteristics of the variables for those outliers. Finally, I demonstrate the application of these methods using the National Health Interview Survey (NHIS) 2008 database for the purposes of studying adolescent obesity.
590 $a School code: 0067.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Statistics. $3 517247
650 4 $a Computer Science. $3 626642
690 $a 0364
690 $a 0463
690 $a 0984
710 2 $a Duquesne University. $b Computational Mathematics. $3 1671744
773 0 $t Masters Abstracts International $g 49-02.
790 1 0 $a D'Amico, Frank, $e advisor
790 1 0 $a Kern, John $e committee member
790 1 0 $a Fleming, John $e committee member
790 $a 0067
791 $a M.S.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1482750