K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data
AM Ikotun, AE Ezugwu, L Abualigah, B Abuhaija… - Information …, 2023 - Elsevier
Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …
Transforming complex problems into K-means solutions
K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …
Image clustering using local discriminant models and global integration
In this paper, we propose a new image clustering algorithm, referred to as clustering using
local discriminant models and global integration (LDMGI). To deal with the data points …
local discriminant models and global integration (LDMGI). To deal with the data points …
Multi-view clustering and feature learning via structured sparsity
Combining information from various data sources has become an important research topic
in machine learning with many scientific applications. Most previous studies employ kernels …
in machine learning with many scientific applications. Most previous studies employ kernels …
Spectral embedded adaptive neighbors clustering
Spectral clustering has been widely used in various aspects, especially the machine
learning fields. Clustering with similarity matrix and low-dimensional representation of data …
learning fields. Clustering with similarity matrix and low-dimensional representation of data …