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 …
Tomato leaf disease identification by restructured deep residual dense network
C Zhou, S Zhou, J Xing, J Song - IEEE Access, 2021 - ieeexplore.ieee.org
As COVID-19 spread worldwide, many major grain-producing countries have adopted
measures to restrict their grain exports; food security has aroused great concern from …
measures to restrict their grain exports; food security has aroused great concern from …
Hierarchical density estimates for data clustering, visualization, and outlier detection
An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …
visualization is introduced in this article. The main module consists of an algorithm to …
Finding compact and well-separated clusters: Clustering using silhouette coefficients
Finding compact and well-separated clusters in data sets is a challenging task. Most
clustering algorithms try to minimize certain clustering objective functions. These functions …
clustering algorithms try to minimize certain clustering objective functions. These functions …
Ward's hierarchical agglomerative clustering method: which algorithms implement Ward's criterion?
F Murtagh, P Legendre - Journal of classification, 2014 - Springer
The Ward error sum of squares hierarchical clustering method has been very widely used
since its first description by Ward in a 1963 publication. It has also been generalized in …
since its first description by Ward in a 1963 publication. It has also been generalized in …
A survey of clustering algorithms for big data: Taxonomy and empirical analysis
Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …
accurately analyze the massive volume of data generated by modern applications. In …
[PDF][PDF] Review on determining number of Cluster in K-Means Clustering
TM Kodinariya, PR Makwana - International Journal, 2013 - researchgate.net
Clustering is widely used in different field such as biology, psychology, and economics. The
result of clustering varies as number of cluster parameter changes hence main challenge of …
result of clustering varies as number of cluster parameter changes hence main challenge of …
[图书][B] Functional and shape data analysis
A Srivastava, EP Klassen - 2016 - Springer
Function and shape data analysis are old topics in statistics, studied off and on over the last
several decades. However, the early years of the new millennium saw a renewed focus and …
several decades. However, the early years of the new millennium saw a renewed focus and …
[HTML][HTML] Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery
Traditional plant breeding based on selection for grain yield is time-consuming and costly;
therefore, new innovative methods are in high demand to reduce costs and accelerate …
therefore, new innovative methods are in high demand to reduce costs and accelerate …
Data mining for the internet of things: literature review and challenges
The massive data generated by the Internet of Things (IoT) are considered of high business
value, and data mining algorithms can be applied to IoT to extract hidden information from …
value, and data mining algorithms can be applied to IoT to extract hidden information from …