Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator
نویسندگان
چکیده
Mahalanobis-type distances in which the shape matrix is derived from a consistent highbreakdown robust multivariate location and scale estimator can be used to 2nd outlying points. Hardin and Rocke (http://www.cipic.ucdavis.edu/∼dmrocke/preprints.html) developed a new method for identifying outliers in a one-cluster setting using an F distribution. We extend the method to the multiple cluster case which gives a robust clustering method in conjunction with an outlier identi2cation method. We provide results of the F distribution method for multiple clusters which have di6erent sizes and shapes. c © 2002 Elsevier B.V. All rights reserved.
منابع مشابه
Outlier Detection for Support Vector Machine using Minimum Covariance Determinant Estimator
The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus...
متن کاملRobustified distance based fuzzy membership function for support vector machine classification
Fuzzification of support vector machine has been utilized to deal with outlier and noise problem. This importance is achieved, by the means of fuzzy membership function, which is generally built based on the distance of the points to the class centroid. The focus of this research is twofold. Firstly, by taking the advantage of robust statistics in the fuzzy SVM, more emphasis on reducing the im...
متن کاملOutlier detection for high-dimensional data
Outlier detection is an integral component of statistical modelling and estimation. For highdimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the...
متن کاملNonsingular Robust Covariance Estimation in Multivariate Outlier Detection
Rousseeuw’s minimum covariance determinant (MCD) method is a highly robust estimator of multivariate mean and covariance. In practice, the MCD covariance estimator may be singular. However, a nonsingular covariance estimator is required to calculate the Mahalanobis distance. In order to fix this singular problem, we propose an improved version of the MCD estimator, which is a combination of the...
متن کاملInvestigating Outliers Detection Methods for the Iranian Manufacturing Establishment Survey Data
The role and importance of the industrial sector in the economic development specify the necessity of having accurate and timely data for exact planning. As outliers data in establishment surveys are common due to the structure of the economy, the evaluation of survey data by identifying and investigating outliers prior to the release of data is necessary. In this paper the practical applicatio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Computational Statistics & Data Analysis
دوره 44 شماره
صفحات -
تاریخ انتشار 2004