Search
Search Results
-
Similarity measures of Pythagorean fuzzy sets based on Lp metric and its applications to multicriteria decision-making with Pythagorean VIKOR and clustering
Distance and similarity measures are popular due to various applications across different fields, including clustering, classification, information...
-
Applicability of a novel Pythagorean fuzzy correlation coefficient in medical diagnosis, clustering, and classification problems
A Pythagorean fuzzy set outperforms fuzzy and intuitionistic fuzzy sets in solving uncertain issues. For comparing Pythagorean fuzzy sets,...
-
Clustering
Clustering is the automatic grou** of data points into subsets of similar points. There are numerous ways to define this problem, and most of them...
-
The structure of the genetic code as an optimal graph clustering problem
The standard genetic code (SGC) is the set of rules by which genetic information is translated into proteins, from codons, i.e. triplets of...
-
Linear Methods: Kernels, Variations, and Averaging
In this chapter, we describe linear methods based on kernels or averaging. Principal component analysis (PCA) is a basic method for dimension...
-
An efficient approach in rainfall prediction around Sathanur Dam, India, by model based clustering, structural equation modelling (SEM) and artificial neural networks (ANN)
In the present paper, the rainfall forecast information is analyzed using model and density based clustering algorithms and good model is fitted by...
-
Approximation Algorithms on k-Correlation Clustering
In this paper, we consider the k -correlation clustering problem. Given an edge-weighted graph G ( V , E ) where the edges are labeled either “
... 
-
A Distributional Approach for Soft Clustering Comparison and Evaluation
The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general...
-
FAFOC: Fog-Based Energy-Efficient Clustering Technique for Wireless Sensor Networks
In recent days, wireless sensor networks (WSN) is commonly employed in IoT applications; it should satisfy the needs of IoT. As classical WSN faces...
-
A resimulation framework for event loss tables based on clustering
Catastrophe loss modeling has enormous relevance for various insurance companies due to the huge loss potential. In practice,...
-
Efficient global optimization method via clustering/classification methods and exploration strategy
The objective of this research is to efficiently solve complicated high dimensional optimization problems by using machine learning technologies....
-
Clustering of Countries Based on the Associated Social Contact Patterns in Epidemiological Modelling
Mathematical models have been used to understand the spread patterns of infectious diseases such as coronavirus disease 2019 (COVID-19). The...
-
ORCA: Outlier detection and Robust Clustering for Attributed graphs
A framework is proposed to simultaneously cluster objects and detect anomalies in attributed graph data. Our objective function along with the...
-
Detection of Moving Object Using Modified Fuzzy C-Means Clustering from the Complex and Non-stationary Background Scenes
Detecting moving things in a video sequence is tough, and reliable moving object identification in video frames for computer vision application is a...
-
Exploring Sign Language Recognition Methods: An Effective Kernel Approach
Universally, sign language is the widely used mode of communication for hearing-impaired people. Several conflicting investigations on recognition...
-
A Stochastic Alternating Balance k-Means Algorithm for Fair Clustering
In the application of data clustering to human-centric decision-making systems, such as loan applications and advertisement recommendations, the...
-
Higher-Order Spectral Clustering for Geometric Graphs
The present paper is devoted to clustering geometric graphs. While the standard spectral clustering is often not effective for geometric graphs, we...
-
Two-Factor DEA Modeling and Clustering of Homogeneous Firms
AbstractThe paper presents a model for clustering homogeneous firms according to their operation efficiency over a certain time period. The firm...
-
Methods for Compositional Data
You work with compounds of a whole (and, of course, including missing values), for example, measurements of parts per million of chemical elements of...
-
A K-Means Clustering-Based Multiple Importance Sampling Algorithm for Integral Global Optimization
In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization...