JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 356
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306207
Title: Clustering-Based Robust Multivariate Outlier Detection
Author(s): Nedret Billor*+ and Gulsen Kiral and Asuman Turkmen
Companies: Auburn University and Cukurova University and The Ohio State University
Address: , Auburn, AL, ,
Keywords: Clustering ; High dimensional data ; Distance-distance plot ; Outlier

In this study, we attempt to develop a method of detecting multivariate outliers that can be applied to data that are expected to have a group structure, although the details of this grouping are not known beforehand. Robust clustering analysis provides an appealing unifying framework for addressing problems of outliers and grouping simultaneously. There are several robust clustering methods. However these are only effective in low dimensional data. In this study, we propose a robust clustering method which is effective for both low and high dimensional data. We restricted our study with only that the clusters are elliptical and the outlier identification methods are calibrated at the multivariate normal. Simulated data and real data examples are used to illustrate the effectiveness of the procedure. In addition, we also propose to use distance-distance graph which is effective in determining whether the majority of the outliers form a separate cluster or whether they are randomly scattered.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.