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A Fuzzy Paradigmatic Clustering Algorithm

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A Fuzzy Paradigmatic Clustering Algorithm. / Amirjavid, Farzad; Barak, Sasan; Nemati, Hamidreza.

In: IFAC-PapersOnLine, Vol. 52, No. 13, 31.12.2019, p. 2360-2365.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Amirjavid, F, Barak, S & Nemati, H 2019, 'A Fuzzy Paradigmatic Clustering Algorithm', IFAC-PapersOnLine, vol. 52, no. 13, pp. 2360-2365. https://doi.org/10.1016/j.ifacol.2019.11.559

APA

Amirjavid, F., Barak, S., & Nemati, H. (2019). A Fuzzy Paradigmatic Clustering Algorithm. IFAC-PapersOnLine, 52(13), 2360-2365. https://doi.org/10.1016/j.ifacol.2019.11.559

Vancouver

Amirjavid F, Barak S, Nemati H. A Fuzzy Paradigmatic Clustering Algorithm. IFAC-PapersOnLine. 2019 Dec 31;52(13):2360-2365. https://doi.org/10.1016/j.ifacol.2019.11.559

Author

Amirjavid, Farzad ; Barak, Sasan ; Nemati, Hamidreza. / A Fuzzy Paradigmatic Clustering Algorithm. In: IFAC-PapersOnLine. 2019 ; Vol. 52, No. 13. pp. 2360-2365.

Bibtex

@article{967c93cddf6c4a45861b37dbcc3afc30,
title = "A Fuzzy Paradigmatic Clustering Algorithm",
abstract = "Clustering algorithms resume the datasets into few number of data points such as centroids or medoids, which explain the entire dataset briefly. In the domain of data-driven machine learning, the more precision with the clustering rule leads directly to more precise classification, prediction, and recognition. We propose an efficient clustering method, which applies the paradigms - mainly 3D Gaussian model - to estimate the optimum cluster number, cluster border, and congestion coordinates to model the datasets of the natural distributions.This approach considers both qualitative and quantitative features of the dataset and calculates the best scale to analyze it. We used fuzzy logic to compare the models with data, to generate and rank the hypotheses, and finally to reject or accept the assumptions. The proposed approach which is called Fuzzy Gaussian Paradigmatic Clustering (FGPC) algorithm is used as the basis of a fast (with the complexity order of O(n)) and robust algorithm for identifying fuzzy models.",
keywords = "Paradigmatic clustering, Fuzzy logic, Gaussian distribution",
author = "Farzad Amirjavid and Sasan Barak and Hamidreza Nemati",
year = "2019",
month = dec,
day = "31",
doi = "10.1016/j.ifacol.2019.11.559",
language = "English",
volume = "52",
pages = "2360--2365",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "IFAC Secretariat",
number = "13",

}

RIS

TY - JOUR

T1 - A Fuzzy Paradigmatic Clustering Algorithm

AU - Amirjavid, Farzad

AU - Barak, Sasan

AU - Nemati, Hamidreza

PY - 2019/12/31

Y1 - 2019/12/31

N2 - Clustering algorithms resume the datasets into few number of data points such as centroids or medoids, which explain the entire dataset briefly. In the domain of data-driven machine learning, the more precision with the clustering rule leads directly to more precise classification, prediction, and recognition. We propose an efficient clustering method, which applies the paradigms - mainly 3D Gaussian model - to estimate the optimum cluster number, cluster border, and congestion coordinates to model the datasets of the natural distributions.This approach considers both qualitative and quantitative features of the dataset and calculates the best scale to analyze it. We used fuzzy logic to compare the models with data, to generate and rank the hypotheses, and finally to reject or accept the assumptions. The proposed approach which is called Fuzzy Gaussian Paradigmatic Clustering (FGPC) algorithm is used as the basis of a fast (with the complexity order of O(n)) and robust algorithm for identifying fuzzy models.

AB - Clustering algorithms resume the datasets into few number of data points such as centroids or medoids, which explain the entire dataset briefly. In the domain of data-driven machine learning, the more precision with the clustering rule leads directly to more precise classification, prediction, and recognition. We propose an efficient clustering method, which applies the paradigms - mainly 3D Gaussian model - to estimate the optimum cluster number, cluster border, and congestion coordinates to model the datasets of the natural distributions.This approach considers both qualitative and quantitative features of the dataset and calculates the best scale to analyze it. We used fuzzy logic to compare the models with data, to generate and rank the hypotheses, and finally to reject or accept the assumptions. The proposed approach which is called Fuzzy Gaussian Paradigmatic Clustering (FGPC) algorithm is used as the basis of a fast (with the complexity order of O(n)) and robust algorithm for identifying fuzzy models.

KW - Paradigmatic clustering

KW - Fuzzy logic

KW - Gaussian distribution

U2 - 10.1016/j.ifacol.2019.11.559

DO - 10.1016/j.ifacol.2019.11.559

M3 - Journal article

VL - 52

SP - 2360

EP - 2365

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 13

ER -