A review of supervised machine learning algorithms
Supervised machine learning is the construction of algorithms that are able to produce
general patterns and hypotheses by using externally supplied instances to predict the fate of …
general patterns and hypotheses by using externally supplied instances to predict the fate of …
Clustering algorithms: their application to gene expression data
Gene expression data hide vital information required to understand the biological process
that takes place in a particular organism in relation to its environment. Deciphering the …
that takes place in a particular organism in relation to its environment. Deciphering the …
Minimum spanning tree hierarchical clustering algorithm: a new Pythagorean fuzzy similarity measure for the analysis of functional brain networks
Clustering structures are one of the most important aspects of complex networks. Minimum
spanning tree (MST), the tree that connects all vertices with minimum total weight, can be …
spanning tree (MST), the tree that connects all vertices with minimum total weight, can be …
Method for determining the optimal number of clusters based on agglomerative hierarchical clustering
S Zhou, Z Xu, F Liu - IEEE transactions on neural networks and …, 2016 - ieeexplore.ieee.org
It is crucial to determine the optimal number of clusters for the clustering quality in cluster
analysis. From the standpoint of sample geometry, two concepts, ie, the sample clustering …
analysis. From the standpoint of sample geometry, two concepts, ie, the sample clustering …
A fast O (NlgN) time hybrid clustering algorithm using the circumference proximity based merging technique for diversified datasets
MM Akhter, SK Mohanty - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Clustering has been widely employed for extracting intrinsic groups because of its low
reliance on domain knowledge. Though several clustering techniques have been developed …
reliance on domain knowledge. Though several clustering techniques have been developed …
Density peak clustering based on relative density relationship
J Hou, A Zhang, N Qi - Pattern Recognition, 2020 - Elsevier
The density peak clustering algorithm treats local density peaks as cluster centers, and
groups non-center data points by assuming that one data point and its nearest higher …
groups non-center data points by assuming that one data point and its nearest higher …
An automatic method to determine the number of clusters using decision-theoretic rough set
Clustering provides a common means of identifying structure in complex data, and there is
renewed interest in clustering as a tool for the analysis of large data sets in many fields …
renewed interest in clustering as a tool for the analysis of large data sets in many fields …
Clustering with local density peaks-based minimum spanning tree
D Cheng, Q Zhu, J Huang, Q Wu… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Clustering analysis has been widely used in statistics, machine learning, pattern recognition,
image processing, and so on. It is a great challenge for most existing clustering algorithms to …
image processing, and so on. It is a great challenge for most existing clustering algorithms to …
Efficient synthetical clustering validity indexes for hierarchical clustering
Q Xu, Q Zhang, J Liu, B Luo - Expert Systems with Applications, 2020 - Elsevier
Clustering validation and identifying the optimal number of clusters are of great importance
in expert and intelligent systems. However, the commonly used similarity measures for …
in expert and intelligent systems. However, the commonly used similarity measures for …
A fast minimum spanning tree algorithm based on K-means
Minimum spanning trees (MSTs) have long been used in data mining, pattern recognition
and machine learning. However, it is difficult to apply traditional MST algorithms to a large …
and machine learning. However, it is difficult to apply traditional MST algorithms to a large …