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Leski J.M.,Silesian University of Technology | Leski J.M.,Institute of Medical Technology and Equipment
Fuzzy Sets and Systems | Year: 2015

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy C-Ordered-Means (FCOM) clustering. This method uses both the Huber's M-estimators and the Yager's OWA operators to obtain its robustness. The proposed method is compared to many other ones, e.g.: the Fuzzy C-Means (FCM), the Possibilistic Clustering (PC), the fuzzy Noise Clustering Method (NCM), the Lp norm clustering (Lp FCM) (0 Source


D'Urso P.,University of Rome La Sapienza | Leski J.M.,Silesian University of Technology | Leski J.M.,Institute of Medical Technology and Equipment
Pattern Recognition | Year: 2016

Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a partitioning around medoids approach. However, one of the greatest disadvantages of this method is its sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber's M-estimators and the Yager's Ordered Weighted Averaging (OWA) operators are used in the method proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided. © 2016 Elsevier Ltd. Source


Smolen M.,AGH University of Science and Technology | Kantoch E.,AGH University of Science and Technology | Augustyniak P.,AGH University of Science and Technology | Kowalski P.,Institute of Medical Technology and Equipment
IFMBE Proceedings | Year: 2011

Advanced developments in existing information technologies are extending the range of treatment and health services available at patient's home. This paper proposes a mobile monitoring system that integrates wearable ECG and ACC mobile sensors. We present an algorithm determining the correlation between the heart rate and movements based on automatic analysis of ACC and ECG signals. The system was tested on seven healthy adults who were asked to perform normal daily activity. As a result, we examined general physical state during normal daily activities of the subject, based on the analysis of sensor signals. © 2011 Springer-Verlag Berlin Heidelberg. Source


Gacek A.,Institute of Medical Technology and Equipment | Gacek A.,University of Alberta | Pedrycz W.,University of Alberta | Pedrycz W.,King Abdulaziz University
IEEE Transactions on Fuzzy Systems | Year: 2015

The study is devoted to the clustering of granular data and an evaluation of the results of such clustering. A comprehensive and systematic approach is developed, which is composed of three fundamental phases: 1) representation of granular data; 2) clustering carried out in the representation space of information granules; and 3) evaluation of quality of clusters following the reconstruction criterion. The reconstruction criterion formed originally for numeric data and leading to an idea of granular prototypes is revisited. We show here an emergence of granular information of higher type, which are used to implement granular interval prototypes. We discuss a way of forming granular data in the context of representation of time series and present clustering of granular time series. © 1993-2012 IEEE. Source


Leski J.M.,Silesian University of Technology | Leski J.M.,Institute of Medical Technology and Equipment | Kotas M.,Silesian University of Technology
Fuzzy Sets and Systems | Year: 2015

One of the most popular clustering methods based on minimization of a criterion function is the fuzzy c-means one. Its generalization by application of hyperplane shaped prototypes of the clusters is known as the Fuzzy C-Regression Models (FCRM) method. Although with this generalization many new applications of clustering emerged, it appeared to be rather sensitive to poor initialization and to the presence of noise and outliers in data. In this paper we introduce a new objective function, using the Huber's M-estimators and the Yager's OWA operators to overcome the disadvantages of the approach considered. We derive and describe an algorithm for minimization of the objective function defined. We have called it the Fuzzy C-Ordered-Regression Models (FCORM) clustering algorithm. The algorithm is compared to a few other important reference ones. To this end experiments on synthetic data with various types of noise and different numbers of outliers are carried out. We investigate the methods performance in the conditions that can be encountered in signal analysis. Large-scale simulations demonstrate the competitiveness and usefulness of the method proposed. © 2014 Elsevier B.V. Source

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