Sains Malaysiana 43(12)(2014): 1973–1977

 

Eigenstructure-Based Angle for Detecting Outliers in Multivariate Data

(Sudut Berasaskan Struktur Eigen untuk Mengesan Titik Terpencil dalam Data Multivariat)

 

 

NAZRINA AZIZ*

UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia

 

Diserahkan: 20 Februari 2013/Diterima: 2 Mei 2014

 

ABSTRACT

There are two main reasons that motivate people to detect outliers; the first is the researchers' intention; see the example of Mr Haldum’s cases in Barnett and Lewis. The second is the effect of outliers on analyses. This article does not differentiate between the various justifications for outlier detection. The aim was to advise the analyst about observations that are isolated from the other observations in the data set. In this article, we introduce the eigenstructure based angle for outlier detection. This method is simple and effective in dealing with masking and swamping problems. The method proposed is illustrated and compared with Mahalanobis distance by using several data sets.

 

Keywords: Angle; Eigenstructure; masking; outliers; swamping

 

ABSTRAK

Terdapat dua sebab utama yang mendorong orang ramai untuk mengesan titik terpencil, yang pertama adalah hasrat penyelidik; lihat contoh kes Encik Haldum di Barnett dan Lewis. Yang kedua adalah kesan titik terpencil ke atas analisis. Kertas ini tidak membezakan antara pelbagai justifikasi untuk mengesan titik terpencil. Tujuannya adalah untuk berkongsi dengan penganalisis mengenai cerapan yang terpencil daripada cerapan lain dalam set data. Dalam kertas ini, kami memperkenalkan sudut berasaskan struktur eigen untuk mengesan titik terpencil. Kaedah ini adalah mudah dan berkesan dalam berurusan dengan masalah litupan dan limpahan. Kaedah yang dicadangkan digambarkan dan dibandingkan dengan jarak Mahalanobis menggunakan beberapa set data.

 

Kata kunci: Limpahan; litupan; struktur eigen; sudut; titik terpencil

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*Pengarang untuk surat-menyurat; email: nazrina@uum.edu.my

 

   

 

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