Comparative Performance Analysis of Ellipsoidal Support Vector Clustering on Biomedical Data Sets

Year 2019, Volume: 7 Issue: 1, 140 - 148, 15.01.2019

Ömer Karal , Furkan Burak Bağcı

https://doi.org/10.21541/apjes.424247

Cited By: 1

Abstract

The performance of clustering algorithms is very
important in biomedical research because they help in the pre-diagnosis of
diseases, recognize diseases and take necessary precautions in diseased people.
However, most clustering algorithms use the Euclidean distance as a similarity
metric. Euclidean distance assumes the variances of the data samples are equal.
The performance of traditional clustering methods that use Euclidean distance
is quite low if the data contains noise or outlier samples. This study proposes
the Ellipsoidal Support Vector Clustering algorithm, which is one of the
kernel-based clustering methods, in order to eliminate the above mentioned
problems. In the ESVC algorithm, there is no need to specify the cluster number
in advance. Moreover, the ESVC algorithm is capable of generating clustering
shapes that are appropriate to the distribution of data using the mahalanobis
similarity metric. The proposed ESVC algorithm was applied to both real
biomedical data and synthetic data and then compared to conventional clustering
methods. It has been observed that ESVC algorithm performs well in terms of
accuracy, specificity and sensitivity.

Keywords

Biomedical data, clustering, ellipsoidal support vector clustering

Elipsoit Destek Vektör Öbekleme Algoritmasının Biyomedikal Veri Setleri Üzerinde Karşılaştırmalı Performans Analizi

Abstract

Hastalıklı kişilerde hastalığın
teşhisinin önceden yapılması, tanısının konulması ve gerekli önlemlerin
alınmasına yardımcı olmalarından dolayı öbekleme algoritmalarının performansı
biyomedikal araştırmalarda çok önemlidir. Ancak, çoğu öbekleme algoritması
benzerlik metriği olarak Öklid uzaklığını kullanır. Öklid uzaklığı verilerin
varyanslarını eşit kabul eder. Gürültülü veya aykırı değerlerin veriye bulaşması
durumunda, geleneksel Öklid uzaklığı kullanan öbekleme yöntemlerinin
performansı oldukça düşmektedir. Bu çalışma, yukarıda bahsedilen olumsuzlukları
gidermek için kernel tabanlı öbekleme yöntemlerinden biri olan Elipsoit Destek
Vektör Öbekleme (EDVÖ) algoritmasını önerir. EDVÖ algoritmasında, önceden öbek
sayısının belirtilmesine gerek yoktur. Ayrıca, EDVÖ algoritması, mahalanobis
benzerlik ölçüsünü kullanarak verilerin dağılımına uygun kümelenme şekilleri
üretebilir. Önerilen EDVÖ algoritması hem gerçek biyomedikal verilere hem de
sentetik verilere uygulanmış ve daha sonra geleneksel kümeleme yöntemleri ile
karşılaştırılmıştır. EDVÖ algoritmasının doğruluk, özgüllük ve duyarlılık
açısından iyi bir performans gösterdiği gözlemlenmiştir.

Keywords

Biyomedikal veriler, öbekleme, elipsoit destek vektör öbekleme

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