Results 291 to 300 of about 940,524 (345)

An Effective and Efficient Algorithm for K-Means Clustering With New Formulation

IEEE Transactions on Knowledge and Data Engineering, 2023
K-means is one of the most simple and popular clustering algorithms, which implemented as a standard clustering method in most of machine learning researches.
F. Nie, Ziheng Li, Rong Wang, Xuelong Li
semanticscholar   +1 more source

K-Means Clustering-Based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition in Human–Robot Interaction

IEEE transactions on industrial electronics (1982. Print), 2023
In this article, K-meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human–robot interaction (HRI).
Luefeng Chen, Kuanlin Wang, Min Li, Min Wu, W. Pedrycz, K. Hirota   +5 more
semanticscholar   +1 more source

k-Means Clustering

, 2023
K-Means clustering.
Armand Joseph D. Esteller (14636924), Jarod E. Bruno (14636927), Ardvin Kester S. Ong (14636918), Thanatorn Chuenyindee (14636936), Kriengkrai Thana (14636939), Reny Nadlifatin (14138838), Yogi Tri Prasetyo (14636921), Kathryn Cheska O. Lagorza (14636930), Satria Fadil Persada (14138826), Lance Edward T. Oli (14636933)   +9 more
openaire   +2 more sources

Efficient Multi-View K-Means Clustering With Multiple Anchor Graphs

IEEE Transactions on Knowledge and Data Engineering, 2023
Multi-view clustering has attracted a lot of attention due to its ability to integrate information from distinct views, but how to improve efficiency is still a hot research topic.
Ben Yang, Zhongheng Li, Xuetao Zhang, F. Nie, Fei Wang   +4 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. Naqvi, A. Rehman, M. Iqbal, T. Saba   +6 more
semanticscholar   +1 more source

K-Means Clustering With Natural Density Peaks for Discovering Arbitrary-Shaped Clusters

IEEE Transactions on Neural Networks and Learning Systems, 2023
Due to simplicity, K-means has become a widely used clustering method. However, its clustering result is seriously affected by the initial centers and the allocation strategy makes it hard to identify manifold clusters. Many improved K-means are proposed
Dongdong Cheng, Jinlong Huang, Sulan Zhang, Shuyin Xia, Guoyin Wang, Jiang Xie   +5 more
semanticscholar   +1 more source

Epidemic K-Means Clustering

2011 IEEE 11th International Conference on Data Mining Workshops, 2011
The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered ...
Giuseppe Di Fatta, Francesco Blasa, Simone Cafiero, Giancarlo Fortino   +3 more
openaire   +2 more sources

Global k-means++: an effective relaxation of the global k-means clustering algorithm

Applied intelligence (Boston), 2022
The k-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers.
Georgios Vardakas, A. Likas
semanticscholar   +1 more source

Multi-View K-Means Clustering With Adaptive Sparse Memberships and Weight Allocation

IEEE Transactions on Knowledge and Data Engineering, 2022
Recently, many real-world applications exploit multi-view data, which is collected from diverse domains or obtained from various feature extractors and reflect different properties or distributions of the data.
Junwei Han, Jinglin Xu, F. Nie, Xuelong Li   +3 more
semanticscholar   +1 more source