Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development

Askari, S.

doi:10.1016/j.eswa.2020.113856

Cited by 187 publications

(65 citation statements)

References 64 publications

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“…The state represented by a set of weighted particles in the prediction step is converted to a GM distribution in the update step. The conversion was implemented using the FWD-EM method [ 36 ] modified using the Fuzzy C-Means method for initialization suggested in [ 42 ], under which a superior performance was demonstrated. This procedure required the FWD-EM algorithm to execute for a fixed number of Gaussian components.…”

Section: Resultsmentioning

confidence: 99%

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Cament

Adams

Barrios

2021

Sensors

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This paper presents a Bayesian filter based solution to the Space Object (SO) tracking problem using simulated optical telescopic observations. The presented solution utilizes the Probabilistic Admissible Region (PAR) approach, which is an orbital admissible region that adheres to the assumption of independence between newborn targets and surviving SOs. These SOs obey physical energy constraints in terms of orbital semi-major axis length and eccentricity within a range of orbits of interest. In this article, Low Earth Orbit (LEO) SOs are considered. The solution also adopts the Partially Uniform Birth (PUB) intensity, which generates uniformly distributed births in the sensor field of view. The measurement update then generates a particle SO distribution. In this work, a Poisson Labeled Multi-Bernoulli (PLMB) multi-target tracking filter is proposed, using the PUB intensity model for the multi-target birth density, and a PAR for the spatial density to determine the initial orbits of SOs. Experiments are demonstrated using simulated SO trajectories created from real Two-Line Element data, with simulated measurements from twelve telescopes located in observatories, which form part of the Falcon telescope network. Optimal Sub-Pattern Assignment (OSPA) and CLEAR MOT metrics demonstrate encouraging multi-SO tracking results even under very low numbers of observations per SO pass.

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Section: Resultsmentioning

confidence: 99%

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Cament

Adams

Barrios

2021

Sensors

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“…The DBSCAN can divide data with high density and irregular shape into clusters, which is widely used in tracks [32], medical treatment [33], teaching [34], modal identification [35], and other fields of anomaly identification and early diagnosis. Compared with conventional clustering methods such as K-means, DBSCAN has many advantages, such as no need to specify the number of clusters in advance, being unaffected by initial values, finding clusters with arbitrary shapes, and identifying noise points [36].…”

Section: Principles Of the Dbscanmentioning

confidence: 99%

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Zhao

et al. 2022

Applied Sciences

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As an essential load of the concrete dam, the abnormal change of uplift pressure directly threatens the safety and stability of the concrete dam. Therefore, it is of great significance to accurately and efficiently excavate the hidden information of the uplift pressure monitoring data to clarify the safety state of the concrete dam. Therefore, in this paper, density-based spatial clustering of applications with noise (DBSCAN) method is used to intelligently identify the abnormal occurrence point and abnormal stable stage in the monitoring data. Then, an application method of measured uplift pressure is put forward to accurately reflect the spatial distribution and abnormal position of uplift pressure in the dam foundation. It is easy to calculate and connect with the finite element method through self-written software. Finally, the measured uplift pressure is applied to the finite element model of the concrete dam. By comparing the structural behavior of the concrete dam under the design and measured uplift pressure, the influence of abnormal uplift pressure on the safety state of the concrete dam is clarified, which can guide the project operation. Taking a 98.5 m concrete arch dam in western China as an example, the above analysis ideas and calculation methods have been verified. The abnormal identification method and uplift pressure applying method can provide ideas and tools for the structural diagnosis of a concrete dam.

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“…The range of fuzzy clustering algorithms is broad enough: fuzzy k-means algorithm, fuzzy c-means (FCM) algorithm, fuzzy decision trees, fuzzy Petri nets, fuzzy associative memory, fuzzy self-organizing maps, and others [31][32][33]. The k-means algorithm, the basis of a more advanced method of fuzzy c-means clustering [34,35], is fundamental. These algorithms became the basis for many other ones in this class, and they have enough multiprogram implementations, for example, the FCM algorithm built into MATLAB.…”

Section: -Literature Reviewmentioning

confidence: 99%

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

et al. 2021

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This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF

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Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development

Cited by 187 publications

References 64 publications

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Space Debris Tracking with the Poisson Labeled Multi-Bernoulli Filter

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

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