Large-scale k-means clustering via variance reduction

Zhao, Yawei; Ming, Yuewei; Liu, Xinwang; Zhu, En; Zhao, Kaikai; Yin, Jianping

doi:10.1016/j.neucom.2018.03.059

Cited by 21 publications

(5 citation statements)

References 3 publications

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“…Seven clustering algorithms from all four types are used to cluster the different locations. Any particular clustering technique requires the number of clusters to be specified as an input, such as K-mean and Mini batch K-mean clustering [16]. For these clustering types, the first elbow curve is generated to get the optimal value of k. Affinity propagation and Mean shift are partitioning-based clusters, but the difference is that these methods don't require the number of clusters to be specified advance.…”

Section: A Backgroundmentioning

confidence: 99%

Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

Rizwan¹,

Iqbal

Khan

et al. 2021

IEEE Access

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Recently groundwater scarcity has accelerated drilling operations worldwide as drilled boreholes are substantial for replenishing the needs of safe drinking water and achieving long-term sustainable development goals. However, the quest for achieving optimal drilling efficiency is ever continued. This paper aims to provide valuable insights into borehole drilling data utilizing the potential of advanced analytics by employing several enhanced cluster analysis techniques to propel drilling efficiency optimization and knowledge discovery. The study proposed an L2-weighted K-mean clustering algorithm in which the mean is computed from transformed weighted feature space. To verify the effectiveness of our proposed L2-weighted K-mean algorithm, we performed a comparative analysis of the proposed work with traditional clustering algorithms to estimate the digging time and depth for different soil materials and land layers. The proposed clustering scheme is evaluated widely used evaluation metrics such as Dunn Index, Davies-Bouldin index (DBI), Silhouette coefficient (SC), and Calinski-Harabaz Index (CHI). The study results highlight the significance of the proposed clustering algorithm as it achieved better clustering results than conventional clustering approaches. Moreover, for facilitation of subsequent learning, achievement of reliable classification, and generalization, we performed feature extraction based on the time interval of the drilling process according to soil material and land layer. We formulated the solution by grouping the extracted features into six different blocks to achieve our desired objective. Each block corresponds to various characteristics of soil materials and land layers. Extracted features are examined and visualized in point cloud space to analyze the water level patterns, depth, and days required to complete the drilling operations.INDEX TERMS data analysis; features extraction; unsupervised learning; machine learning; strategic planning and management;

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Section: A Backgroundmentioning

confidence: 99%

Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

Rizwan¹,

Iqbal

Khan

et al. 2021

IEEE Access

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“…Compared to the traditional clustering methods, it has a convex formulation, leading to a robust clustering result. For example, the clustering result of k-means is sensitive to the seeds, and picking good seeds is challenging [3], [14]. However, due to a convex objective function, the result of convex clustering can be determined.…”

Section: Convex Clusteringmentioning

confidence: 99%

Simultaneous Clustering and Optimization for Evolving Datasets

Zhao

Zhu

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solve this problem and obtain the optimal model. However, when a dataset evolves over time, those existing methods have to update the model frequently to guarantee accuracy; such updating is computationally infeasible. In this paper, we propose a new formulation of SCO to handle evolving datasets. Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently. The guarantee of model accuracy is analyzed theoretically for two specific tasks: ridge regression and convex clustering. Extensive empirical studies confirm the effectiveness of our method.

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“…It is common for existing kernel-based methods to reveal the underlying structure of data by calculating the pairwise similarity between samples. However, in many successful machine learning algorithms, such as dimensionality reduction [34], [35], clustering [16], [36], and recent feature selection algorithms [32], [37], [38], researchers find that it is beneficial to preserve only the reliable local geometry as a representation of the data structure. There are two main underlying reasons.…”

Section: A Construction Of the Neighbor Kernelmentioning

confidence: 99%

Multiple Kernel Clustering With Neighbor-Kernel Subspace Segmentation

Zhou

Liu

Li³

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

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Multiple kernel clustering (MKC) has been intensively studied during the last few decades. Even though they demonstrate promising clustering performance in various applications, existing MKC algorithms do not sufficiently consider the intrinsic neighborhood structure among base kernels, which could adversely affect the clustering performance. In this paper, we propose a simple yet effective neighbor-kernel-based MKC algorithm to address this issue. Specifically, we first define a neighbor kernel, which can be utilized to preserve the block diagonal structure and strengthen the robustness against noise and outliers among base kernels. After that, we linearly combine these base neighbor kernels to extract a consensus affinity matrix through an exact-rank-constrained subspace segmentation. The naturally possessed block diagonal structure of neighbor kernels better serves the subsequent subspace segmentation, and in turn, the extracted shared structure is further refined through subspace segmentation based on the combined neighbor kernels. In this manner, the above two learning processes can be seamlessly coupled and negotiate with each other to achieve better clustering. Furthermore, we carefully design an efficient iterative optimization algorithm with proven convergence to address the resultant optimization problem. As a by-product, we reveal an interesting insight into the exact-rank constraint in ridge regression by careful theoretical analysis: it back-projects the solution of the unconstrained counterpart to its principal components. Comprehensive experiments have been conducted on several benchmark data sets, and the results demonstrate the effectiveness of the proposed algorithm.

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Large-scale k-means clustering via variance reduction

Cited by 21 publications

References 3 publications

Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

Toward Effective Pattern Recognition Based on Enhanced Weighted K-Mean Clustering Algorithm for Groundwater Resource Planning in Point Cloud

Simultaneous Clustering and Optimization for Evolving Datasets

Multiple Kernel Clustering With Neighbor-Kernel Subspace Segmentation

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