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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
Abstract:

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.


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