Results 171 to 180 of about 17,769,086 (225)

Transforming Complex Problems Into K-Means Solutions

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
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.
Hongfu Liu, Junxiang Chen, Jennifer G. Dy, Y. Fu   +3 more
semanticscholar   +1 more source

Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification

Microscopy research and technique (Print), 2021
Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification.
A. Khan, Siraj Khan, M. Harouni, R. Abbasi, S. Iqbal, Zahid Mehmood   +5 more
semanticscholar   +1 more source

Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering

Microscopy research and technique (Print), 2021
Melanoma skin cancer is the most life‐threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively ...
Marriam Nawaz, Zahid Mehmood, Tahira Nazir, R. A. Naqvi, A. Rehman, M. Iqbal, T. Saba   +6 more
semanticscholar   +1 more source

Dynamic Coverage Control Based on K-Means

IEEE transactions on industrial electronics (1982. Print), 2022
In this article, we propose the dynamic coverage control method based on K-means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires
Ieee Dengxiu Yu Member, Hao Xu, Ieee C Philip Chen Fellow, Wenjie Bai, Ieee Zhen Wang Member, C. L. P. Chen   +5 more
semanticscholar   +1 more source

Subspace K-means clustering

Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E., Ceulemans, Eva, De Roover, Kim, Van Leeuwen, Karla   +3 more
openaire   +2 more sources

Fuzzy K-Means Clustering With Discriminative Embedding

IEEE Transactions on Knowledge and Data Engineering, 2022
Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function.
F. Nie, Xiaowei Zhao, Rong Wang, Xuelong Li, Zhihui Li   +4 more
semanticscholar   +1 more source

A Unified Form of Fuzzy C-Means and K-Means algorithms and its Partitional Implementation

Knowledge-Based Systems, 2021
This paper proposes as an element of novelty the Unified Form (UF) clustering algorithm, which treats Fuzzy C-Means (FCM) and K-Means (KM) algorithms as a single configurable algorithm.
I. Borlea, Radu-Emil Precup, Alexandra-Bianca Borlea, D. Iercan   +3 more
semanticscholar   +1 more source

PERFORMANCE COMPARISON OF K-MEANS, PARALLEL K-MEANS AND K-MEANS++

K-means clustering is a fundamental unsupervised machine learning technique widely applied in various domains such as data analysis, pattern recognition, and clustering-based tasks. However, its efficiency and scalability can be challenged, particularly when dealing with large-scale datasets and complex data structures.
Aliguliyev, Ramiz, Shalala F. Tahirzada
openaire   +1 more source

Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food

Jurnal Ilmu Komputer dan Sistem Informasi
Abstrak   Terciptanya banyak bisnis di bidang penjualan berbasis online atau yang dikenal dengan istilah e-commerce menjadi bukti bahwa teknologi internet saat ini berkembang begitu pesat di berbagai industri, termasuk bisnis.
Lutfhia Azzahra, Amru Yasir, Kata Kunci, K-Means Clustering, Toko Online, Makanan Beku   +5 more
semanticscholar   +1 more source

Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019)

, 2020
Clustering is an explorative data analysis technique used for investigating the underlying structure in the data. It described as the grouping of objects, where the objects share similar characteristics. Over the past 50 years, clustering has been widely
P. Govender, V. Sivakumar
semanticscholar   +1 more source