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On-line Robust Fuzzy Clustering for Anomalies Detection

  • Yevgeniy Bodyanskiy
  • Oleksii Didyk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

Widly-used fuzzy c-means algorithm (FCM) has been utilized, with much success, in a variety of applications. The algorithm is known as an objective function based fuzzy clustering technique that extends the use of classical k-means method to fuzzy partitions. However, one of the most important drawbacks of this method is its sensitivity to noise and outliers in data since the objective function is the sum of squared distance. New robust fuzzy clustering algorithm (RFC) for exploring of signals of different nature taking into account the presence of noise with unknown density distributions and anomalous outliers in the data being analyzed is presented in this paper. By rejection of the Euclidean distance in the objective function the insensibility to the noise and outliers in the data was archived. Our approach introduces a robust probabilistic clustering procedure and is based on a modified objective function.

Keywords

Robust fuzzy clustering Fuzzy c-means Anomalies detection 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Kharkiv National University of Radio ElectronicsKharkivUkraine
  2. 2.Kherson National Technical UniversityKhersonUkraine

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