Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development
S Askari - Expert Systems with Applications, 2021 - Elsevier
Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …
A review of clustering techniques and developments
This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …
developments made at various times. Clustering is defined as an unsupervised learning …
A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm
C Shi, B Wei, S Wei, W Wang, H Liu, J Liu - EURASIP journal on wireless …, 2021 - Springer
Clustering, a traditional machine learning method, plays a significant role in data analysis.
Most clustering algorithms depend on a predetermined exact number of clusters, whereas …
Most clustering algorithms depend on a predetermined exact number of clusters, whereas …
A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
AE Ezugwu, AM Ikotun, OO Oyelade… - … Applications of Artificial …, 2022 - Elsevier
Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …
active research in many fields of study, such as computer science, data science, statistics …
A comprehensive survey of clustering algorithms
Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …
communication science, computer science and biology science. Clustering, as the basic …
Big data analytics and application for logistics and supply chain management
This special issue explores big data analytics and applications for logistics and supply chain
management by examining novel methods, practices, and opportunities. The articles present …
management by examining novel methods, practices, and opportunities. The articles present …
Hybrid fruit-fly optimization algorithm with k-means for text document clustering
The fast-growing Internet results in massive amounts of text data. Due to the large volume of
the unstructured format of text data, extracting relevant information and its analysis becomes …
the unstructured format of text data, extracting relevant information and its analysis becomes …
A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets
Image segmentation is an essential phase of computer vision in which useful information is
extracted from an image that can range from finding objects while moving across a room to …
extracted from an image that can range from finding objects while moving across a room to …
An overview of fairness in clustering
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that
feature ubiquitously in modern data science, and play a key role in many learning-based …
feature ubiquitously in modern data science, and play a key role in many learning-based …
Survey of clustering algorithms
Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …
analysis, primitive exploration with little or no prior knowledge, consists of research …