An introduction to hesitant fuzzy data clustering

An online method of reliable fuzzy clustering is proposed, designed to analyze data sequentially received for processing. A feature of the developed approach is the use of the membership function of a special kind described by the density function of the Cauchy distribution. The actual procedure for clarifying the centroids of clusters is essentially a self-learning rule “The Winner Takes More” (WTM), in which the neighborhood function is generated by the introduced membership function.

Download Full-text

A Comparative Analysis Between Crisp and Fuzzy Data Clustering Approaches for Traditional and Bioinspired Algorithms

Lecture Notes in Computer Science - Intelligent Data Engineering and Automated Learning – IDEAL 2020 ◽

10.1007/978-3-030-62365-4_29 ◽

2020 ◽

pp. 298-308

Author(s):

Amanda Coradini ◽

Alexandre Szabo

Keyword(s):

Comparative Analysis ◽

Data Clustering ◽

Fuzzy Data ◽

Bioinspired Algorithms

Download Full-text

FUZZY DATA CLUSTERING IN THE RANK SCALE BASED ON A DOUBLE NEO-FUZZY NEURON

Radio Electronics Computer Science Control ◽

10.15588/1607-3274-2017-1-9 ◽

2017 ◽

Vol 0 (1) ◽

Author(s):

Hu Zhengbing ◽

Ye. V. Bodyanskiy ◽

O. K. Tyshchenko ◽

V. O. Samitova

Keyword(s):

Data Clustering ◽

Fuzzy Data

Download Full-text

Balanced Data Clustering Algorithm for Both Hard and Soft Clustering

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i2.176183 ◽

2018 ◽

Vol 6 (2) ◽

pp. 176-183

Author(s):

Purnendu Das ◽

◽

Bishwa Ranjan Roy ◽

Saptarshi Paul ◽

◽

...

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Soft Clustering

Download Full-text

Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/2017.ihsciconf.1811 ◽

2018 ◽

pp. 397

Author(s):

Nadia Hashim Al-Noor ◽

Shurooq A.K. Al-Sultany

Keyword(s):

Maximum Likelihood ◽

Expectation Maximization ◽

Mean Squared Error ◽

Maximum Likelihood Estimators ◽

Reliability Function ◽

Fuzzy Data ◽

Monte Carlo Simulation Study ◽

Weibull Parameters ◽

Squared Error ◽

Newton Raphson

In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson” and the “Expectation-Maximization” techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

Download Full-text