Geodesic distance based fuzzy c-medoid clustering – searching for central points in graphs and high dimensional data

Király, András; Vathy-Fogarassy, Ágnes; Abonyi, János

doi:10.1016/j.fss.2015.06.022

Cited by 14 publications

(9 citation statements)

References 15 publications

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“…To determine the locations of the additional sensors two approaches are proposed inspired by Clustering Large Applications based on Simulated Annealing algorithm (CLASA) [ 37 ] and the Geodesic Distance-based Fuzzy c -Medoid Clustering method (GDFCM) [ 40 ]. In the first algorithm, the CLASA algorithm is modified as follows.…”

Section: Sensor Placement To Ensure Observability and Minimal Relamentioning

confidence: 99%

“…The search mechanism should be fine-tuned as the medoids of the fixed sensors (

) have fixed positions to ensure the observability of the system. The second algorithm enhances the random search of the resulted mCLASA algorithm by the introduction of distance-dependent selection probability calculated based on the Geodesic Distance-based Fuzzy c -Medoid Clustering method (GDFCM) [ 40 ].…”

Section: Sensor Placement To Ensure Observability and Minimal Relamentioning

confidence: 99%

“…The second method utilizes the GDFCM method [ 40 ] to extend the previously introduced mCLASA method. This extension increases the probability that in each iteration potentially better neighboring sensors are selected to replace the sensors placed in the previous iteration steps

.…”

Section: Sensor Placement To Ensure Observability and Minimal Relamentioning

confidence: 99%

See 2 more Smart Citations

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

Leitold

Vathy-Fogarassy

Abonyi

2018

Sensors

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Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and, although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors.

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Section: Sensor Placement To Ensure Observability and Minimal Relamentioning

confidence: 99%

“…The search mechanism should be fine-tuned as the medoids of the fixed sensors (

Section: Sensor Placement To Ensure Observability and Minimal Relamentioning

confidence: 99%

See 1 more Smart Citation

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

Leitold

Vathy-Fogarassy

Abonyi

2018

Sensors

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“…About the geodesic distance popularity among new articles, we should refer to Murari et al (2013). Király, Vathy-Fogarassy, and Abonyi (2016) assumed a non-linear manifold using the geodesic distance between data points and used C-Medoid as the clustering algorithm. Chen et al (2015) proposed K-means clustering with geodesic distance.…”

Section: Related Workmentioning

confidence: 99%

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

2018

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Different clustering approaches consider different aspects of quality including accuracy of cluster formation, speed of execution, and resource requirements. A major issue in data clustering is to consider the density of clusters and their structure. A challenge is to relax any assumption on the clusters shape such as sphericity, which is common in most partitioning approaches such as K‐means. In this article, with the help of the coalitional game theory and the concept of geodesic distance calculation, a density‐based shape‐independent clustering approach is proposed. In addition to the emphasis on the application of game theory, we also pay attention to the relative neighbourhood of data, which is depicted using geodesic distance. Geodesic distance is a well‐known measure for finding the manifold of data. The new idea supposes to discover any embedded structure of data and avoid finding only spherical clusters. The proposed approach is evaluated on a number of standard University of California, Irvine (UCI) datasets, and the results show the effectiveness of the proposed approach in comparison with some other approaches.

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“…Shapiro [32] proposed the merge of neural networks, fuzzy logic, and genetic algorithms. Kraily et al [23] have utilized k-NN graph of high dimensional data as efficient representation of the hidden structure of the clustering problem. Cluster centers are fine-tuned by minimizing fuzzy-weighted geodesic distances.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Parameters Estimation via Hybrid Methods

Danesh¹

2016

HJMS

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Fuzzy regression analysis is one of the most widely used statistical techniques which represents the relation between variables. In this paper, the crisp inputs and the symmetrical triangular fuzzy output are considered. Two hybrid algorithms are considered to fit the fuzzy regression model. In this study, algorithms are based on adaptive neuro-fuzzy inference system. The results are derived based on the V-fold cross validation, so that the validity and quality of the suggested methods can be guaranteed. Finally, using the numerical examples, the performance of the suggested methods are compared with the other ones, such as linear programming (LP) and quadratic programming (QP). Based on examples, hybrid methods are verified for the prediction.

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Geodesic distance based fuzzy c-medoid clustering – searching for central points in graphs and high dimensional data

Cited by 14 publications

References 15 publications

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

A geodesic distance‐based approach for shape‐independent data clustering using coalitional game

Fuzzy Parameters Estimation via Hybrid Methods

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