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A cluster validation index for GK cluster analysis based on relative degree of sharing
2004
Information Sciences
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In this paper, the problem of traditional validity indices when applied to the Gustafson-Kessel (GK) clustering are reviewed. A new cluster validity index for the GK algorithm is proposed. ...
This validity index is defined as the average value of the relative degrees of sharing of all possible pairs of fuzzy clusters in the system. ...
The proposed validity index Now we propose a new validity index based on the relative degree of sharing. ...
doi:10.1016/j.ins.2004.02.006
fatcat:6kb75seawjdeflb5x56t2jjqom
Analysis of privacy profiles applying fuzzy clustering techniques
2019
Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
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The analysis provides the clustering validity procedures applied to the data and then produces the partitioning results of the given set of data in the form of graphical visualizations. ...
This work demonstrates how differently clustering algorithms behave with a given dataset producing various shapes and properties of clusters. ...
Acknowledgement The support of Aigul Kaskina was provided by the Information System Research Group under the direction of Prof. Dr. Andreas Meier, University of Fribourg, Switzerland. ...
doi:10.2991/eusflat-19.2019.37
dblp:conf/eusflat/KaskinaT19
fatcat:oyxu6flas5abtmwcn7eihsej4e
A new validation criteria for type-2 fuzzy c-means and possibilistic c-means
2012
2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS)
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Cluster validation is a major issue in cluster analysis. Different cluster validity indexes for type-1 fuzzy clustering have been proposed in the literature so far. ...
A small value of the index indicates a partition in which the clusters are similar to a lesser degree. ...
[18] proposed a cluster validity index for the Gustafson-Kessel (GK) clustering algorithm that quantifies the relationship between each pair of clusters by calculating the relative degree of sharing ...
doi:10.1109/nafips.2012.6291067
fatcat:lddqc76p2jdd3lttmlokwprpqq
Physiological clustering of visual channels in the mouse retina
2011
Journal of Neurophysiology
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ACKNOWLEDGMENTS We thank Kim Fetchel, David Balya, Tamas Szikra, and Janine Hall for carefully reading the manuscript. Present address for R. ...
Masland: Howe Laboratory of Ophthalmology, Department of Ophthalmology, Mass. Eye and Ear Infirmary, Boston, Massachusetts. ...
Our final judgment of each algorithm was based on the combined results from a group of validity indexes; the average of the normalized values of each validation index, where 1 indicated the best solution ...
doi:10.1152/jn.00331.2010
pmid:21273316
pmcid:PMC3075295
fatcat:na3gu33exzhkti557xz35o2ax4
Design and analysis of experiments in ANFIS modeling for stock price prediction
2011
International Journal of Industrial Engineering Computations
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ANN learning algorithms can be employed for optimization of parameters in a fuzzy system. ...
At the computational point of view, a fuzzy system has a layered structure, similar to an artificial neural network (ANN) of the radial basis function type. ...
For this purpose, three different cluster validity indexes are tested. ...
doi:10.5267/j.ijiec.2011.01.001
fatcat:hks4fefyrjhmzckvrmu7lgkone
New monitoring method based principal component analysis and fuzzy clustering
English
2013
International Journal of Physical Sciences
English
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A comparative study between the classification performance by clustering algorithms and the principal component analysis has been proposed. ...
Locating parameters in defect is based on the technique of fault direction in partial least square. ...
The validation parameters of the FCM, GK and GG clustering algorithms are gathered in the Table 3 . ...
doi:10.5897/ijps12.242
fatcat:imwtehkc4rhpbiqpov6gx6hwqe
Word Embeddings and Validity Indexes in Fuzzy Clustering
[article]
2022
arXiv
pre-print
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In this study, we perform a fuzzy-based analysis of various vector representations of words, i.e., word embeddings. ...
We evaluate results of experiments with various clustering validity indexes to compare different algorithm variation with different embeddings accuracy. ...
results based on EmbeddingFor comparison of Validity Indexes, we gather data in below table with this manner. ...
arXiv:2205.06802v1
fatcat:7e755ztnnrgetierk4mzvta3ee
Rolling Bearing Fault Diagnosis Using a Deep Convolutional Autoencoding Network and Improved Gustafson–Kessel Clustering
2020
Shock and Vibration
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A novel method based on a deep convolutional autoencoding network (DCAEN) and adaptive nonparametric weighted-feature extraction Gustafson–Kessel (ANW-GK) clustering algorithm was developed for the fault ...
Finally, the low-dimensional features are input ANW-GK clustering for fault identification. Two datasets were used to validate the effectiveness of the proposed method. ...
Conclusions A method based on the DCAEN and ANW-GK clustering for rolling bearing fault diagnosis is proposed in this paper. ...
doi:10.1155/2020/8846589
fatcat:ci6zfdcmujcj7fwc3lq77hbbh4
A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters
2016
Computational Intelligence and Neuroscience
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Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged ...
on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function ...
Kim et al. proposed a clustering validity index for GK algorithm based on the average value of the relative degrees of sharing of all possible pairs of fuzzy clusters [20] . ...
doi:10.1155/2016/2647389
pmid:28042291
pmcid:PMC5153549
fatcat:gqd7fit4mrdb5cebnlqnci2zwu
Diversity control for improving the analysis of consensus clustering
2016
Information Sciences
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This method is important for facilitating the analysis of the impact of ensemble diversity in consensus clustering. ...
Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated ...
In the past decade, consensus clustering (or cluster ensembles) has emerged as a powerful approach for mitigating the issues of conventional cluster 2 analysis. ...
doi:10.1016/j.ins.2016.04.027
fatcat:rjaqrxevmfaovgl34iomzqac7y
Transcriptomic Responses of Skeletal Muscle to Acute Exercise in Diabetic Goto-Kakizaki Rats
2019
Frontiers in Physiology
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Our results provide mechanistic insight into the beneficial effects of exercise on hyperglycemia and insulin action in skeletal muscle of diabetic GK rats. ...
After a single bout of running, we found 291 and 598 genes that were differentially expressed in the exercise GK and exercise Wistar rats when compared with the corresponding sedentary rats. ...
This identified three major groups, presented in heat map based on patterns of expression (A), and graphical format based on the mean value of z-score values of gene expression in cluster 1 (B), cluster ...
doi:10.3389/fphys.2019.00872
pmid:31338039
pmcid:PMC6629899
fatcat:c7khiehd35gmnhu5boiue573o4
Clonal selection based fuzzy C-means algorithm for clustering
2014
Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO '14
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several clusters that are associated with a certain membership degree. ...
In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. ...
The cluster validity is a measure of the relative performance of a partitioned structure of the data set. ...
doi:10.1145/2576768.2598270
dblp:conf/gecco/Ludwig14
fatcat:6ufic5zvlvexvkbqme3qiw5dda
Feature-Based Classification of Networks
[article]
2016
arXiv
pre-print
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to classify these networks into categories based on their features at various structural levels. ...
This occurs presumably because networks representing similar purposes or constructions would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible ...
We thank Kimberly Glass and members of the Onnela lab for their feedback and useful discussion. We also acknowledge Nic Larsen, Natalie Stanley, and Sean Xiao for helping identify ...
arXiv:1610.05868v1
fatcat:5sqeo7be6bg53p2mxo46pmtt7e
Penalized model-based clustering of fMRI data
[article]
2020
arXiv
pre-print
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To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network ...
To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity ...
Acknowledgments Conflict of Interest: None declared. ...
arXiv:2010.06408v1
fatcat:sfjzmun2wjdidliesyddgi45ha
A Geospatial Consumer Marketing Campaign Optimization Strategy: Case Of Fuzzy Approach In Nigeria Mobile Market
2018
Zenodo
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Firstly, a fuzzy analytic network using a self-organizing feature map clustering technique based on inputs from managers and literature, which depicts the interrelationships amongst ground realities is ...
Getting the consumer marketing strategy right is a crucial and complex task for firms with a large customer base such as mobile operators in a competitive mobile market. ...
One of the advantages of GK algorithm is that it adapts the clusters according to the real shape of the cluster. ...
doi:10.5281/zenodo.1475037
fatcat:uib3hfs5ebaofaqlf4ji2lffyi
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