IEEE

An Enhanced Density Peak Clustering Algorithm With Dimensionality Reduction and Relative Density Normalization for High-Dimensional Duplicate Data

Abstract: Cluster analysis is a fundamental method for studying big data problems, as it groups samples based on shared features. In cluster analysis, a particular class of big data problems is ...
GitHub

guanjiesun/common-clustering-algorithms

examples_distance.dat is one of the supplementary files in "Clustering by fast search and find of density peaks "sample.txt is an example dataset with 4000 instances and each instance has two features ...
Scientific Research Publishing

Cai, W., Chen, S. and Zhang, D. (2007) Fast and Robust Fuzzy C-Means Clustering Algorithms Incorporating Local Information for Image Segmentation. Pattern Recognition, 40, 825-838.

ABSTRACT: Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering ...
techtimes

Leveraging Machine Learning Algorithms to Combat Fraud in Supply Chain Operations

Abstract: Fraud in supply chain operations poses significant risks to businesses, including financial losses, operational inefficiencies, and erosion of stakeholder trust. With the increasing ...
Scientific Research Publishing

Wani, A.A. (2024) Comprehensive Analysis of Clustering Algorithms: Exploring Limitations and Innovative Solutions. PeerJ Computer Science, 10, e2286.

ABSTRACT: Predicting the material stability is essential for accelerating the discovery of advanced materials in renewable energy, aerospace, and catalysis. Traditional approaches, such as Density ...
New Atlas

Torque Clustering: "Autonomous AI on the horizon"

About every 10 minutes, it seems, a new article about a "revolutionary breakthrough" in AI hits my screen. A new approach, a new feature, billions of dollars this, AI agents that. It has been non-stop ...