WSEAS Transactions on Information Science and Applications
International Journal of Innovative Computing and Applications
Robust data clustering by learning multi-metric Lq-norm distances
Expert Systems with Applications: An International Journal
Finding ensembles of neurons in spike trains by non-linear mapping and statistical testing
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Fuzzy and possibilistic clustering for fuzzy data
Computational Statistics & Data Analysis
Effective FCM noise clustering algorithms in medical images
Computers in Biology and Medicine
Robust kernelized approach to clustering by incorporating new distance measure
Engineering Applications of Artificial Intelligence
Robust constrained fuzzy clustering
Information Sciences: an International Journal
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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.