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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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The global k-means clustering algorithm
Pattern Recognition, 2003 We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions.
Aristidis Likas, Nikos Vlassis
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The global Minmax k-means algorithm. [PDF]
Springerplus, 2016 The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances.
Wang X, Bai Y.
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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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