A Fuzzy Paradigmatic Clustering Algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10243412" target="_blank" >RIV/61989100:27510/19:10243412 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/journal/ifac-papersonline/vol/52/issue/13" target="_blank" >https://www.sciencedirect.com/journal/ifac-papersonline/vol/52/issue/13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2019.11.559" target="_blank" >10.1016/j.ifacol.2019.11.559</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Fuzzy Paradigmatic Clustering Algorithm

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Fuzzy Paradigmatic Clustering Algorithm

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA18-15530S" target="_blank" >GA18-15530S: Multi Objective Optimization Application in Flexible Manufacturing and Project Scheduling Problems: Theory and Applications</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IFAC-PapersOnLine. Volume 52, Issue 13

ISBN

—

ISSN

2405-8963

e-ISSN

—

Počet stran výsledku

6

Strana od-do

2360-2365

Název nakladatele

Elsevier

Místo vydání

Amsterdam

Místo konání akce

Berlín

Datum konání akce

28. 8. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000504282400400