TY  - JOUR
ID  - Rehm_et_al_noise_clustering_2007
T1  - A novel approach to noise clustering for outlier detection
A1  - Rehm, Frank
A1  - Klawonn, Frank
A1  - Kruse, Rudolf
JA  - Soft Computing - A Fusion of Foundations, Methodologies and Applications
Y1  - 2007
VL  - 11
IS  - 5
SP  - 489
EP  - 494
SN  - 1432-7643
UR  - http://springerlink.metapress.com/content/3310528547m1w712/?p=f8005f795c024d11941f0610f853aae4&pi=7
M2  - doi: 10.1007/s00500-006-0112-4
KW  - Fuzzy clustering
KW  - Noise clustering
KW  - Outlier detection
N2  - Noise clustering, as a robust clustering method, performs partitioning of data sets reducing errors caused by outliers. Noise clustering defines outliers in terms of a certain distance, which is called noise distance. The probability or membership degree of data points belonging to the noise cluster increases with their distance to regular clusters. The main purpose of noise clustering is to reduce the influence of outliers on the regular clusters. The emphasis is not put on exactly identifying outliers. However, in many applications outliers contain important information and their correct identification is crucial. In this paper we present a method to estimate the noise distance in noise clustering based on the preservation of the hypervolume of the feature space. Our examples will demonstrate the efficiency of this approach.
ER  -