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Unsupervised K-Means Clustering Algorithm [PDF]
IEEE Access, 2020 The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the
Kristina P. Sinaga, Miin-Shen Yang
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Adaptive Initialization Method for K-Means Algorithm [PDF]
Frontiers in Artificial Intelligence, 2021 The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local ...
Jie Yang +4 more
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Clustering Using Boosted Constrained k-Means Algorithm [PDF]
Frontiers in Robotics and AI, 2018 This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle.
Masayuki Okabe, Seiji Yamada
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An Improved K-Means Algorithm Based on Evidence Distance [PDF]
Entropy, 2021 The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points.
Ailin Zhu +4 more
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Reducing the time requirement of k-means algorithm. [PDF]
PLoS ONE, 2012 Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d.
Victor Chukwudi Osamor +3 more
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Iris Recognition Using Image Moments and k-Means Algorithm [PDF]
The Scientific World Journal, 2014 This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm.
Yaser Daanial Khan +3 more
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Stable K Multiple-Means Clustering Algorithm
Jisuanji kexue yu tansuo, 2021 For improving the performance of K-means on the nonconvex cluster, a multiple-means clustering method with specified K clusters partitions the original data into multiple subclasses, and formalizes the multiple-means clustering problem as an optimization
ZHANG Nini, GE Hongwei
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An Overlapping Subspace K-Means Clustering Algorithm [PDF]
Jisuanji gongcheng, 2020 Most of existing clustering algorithms for high-dimensional sparse data do not consider overlapping class clusters and outliers,resulting in unsatisfactory clustering results.Therefore,this paper proposes an overlapping subspace K-Means clustering ...
LIU Yuhang, MA Huifang, LIU Haijiao, YU Li
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Unsupervised Multi-View K-Means Clustering Algorithm
IEEE Access, 2023 Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected.
Miin-Shen Yang, Ishtiaq Hussain
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An improved K‐means algorithm for big data
IET Software, 2022 An improved version of K‐means clustering algorithm that can be applied to big data through lower processing loads with acceptable precision rates is presented here.
Fatemeh Moodi, Hamid Saadatfar
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