Clustering with Local Density Peaks-Based Minimum Spanning Tree

Cheng, Dongdong; Zhu, Qingsheng; Huang, Jinlong; Wu, Quanwang; Yang, Lijun

doi:10.1109/tkde.2019.2930056

Cited by 88 publications

(30 citation statements)

References 41 publications

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“…In the past decades, clustering has been extensively studied from different aspects. Many clustering approaches have been developed, such as k-means clustering [16], spectral clustering [5]- [8], hierarchical clustering [17], subspace clustering [18], density-based clustering [19], [20]. Besides the single clustering methods, some ensemble clustering techniques have been proposed [21]- [24].…”

Section: Related Workmentioning

confidence: 99%

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Fast Incremental Spectral Clustering in Titanate Application via Graph Fourier Transform

Shu-Juan

2020

IEEE Access

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In numerous applications the rapid increase of data size with time makes classical clustering algorithms too slow because of the high computational cost. In the present contribution, a novel method for online spectral clustering algorithm is introduced, which can be applied for simulation of structure and properties of titanate in chemical engineering. The proposed algorithm uses the recent results in the emerging field of graph signal processing. It avoids the costly computation of the eigenvectors by filtering random signals on the graph, and only a few derivative operations are needed to update the clustering result when a new data arrives. Initial simulations are presented using both simulated and real data sets, illustrating the relevance of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

“…Let E = L t+1 − L t , and the gradient of the function g (a third-order tensor) is denoted as Q = ∂g ∂L t (20) where…”

Section: B Incremental Strategymentioning

confidence: 99%

Fast Incremental Spectral Clustering in Titanate Application via Graph Fourier Transform

Shu-Juan

2020

IEEE Access

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“…These clustering algorithms can be classified into four main categories; distance and similarity-based, hierarchical, squared error-based and graph theory-based algorithms [10]. In addition, there is a class of density-based clustering algorithms [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

et al. 2021

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Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters. INDEX TERMSClustering algorithm, content multicasting, D2D enabled networks, deep neural networks, eNB loading, machine learning, random forest, support vector machine, user segregation.

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“…The framework of DDC is to determine, based on the distance function, the local density of each data point and the shortest distance among each data point and other data points with higher local density are computed to construct the decision graph first, then the cluster centers based on the decision graph are selected, and finally, the remaining data points are placed into the nearest cluster with higher local density. As the two indicators are easy to compute and effective, DDC has been widely used [15][16][17]. However, in most cases, the centers of the data set are not very obvious.…”

Section: Introductionmentioning

confidence: 99%

Fast Clustering by Affinity Propagation Based on Density Peaks

2020

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Clustering is an important technique in data mining and knowledge discovery. Affinity propagation clustering (AP) and density peaks and distance-based clustering (DDC) are two significant clustering algorithms proposed in 2007 and 2014 respectively. The two clustering algorithms have simple and clear design ideas, and are effective in finding meaningful clustering solutions. They have been widely used in various applications successfully. However, a key disadvantage of AP is its high time complexity, which has become a bottleneck when applying AP for large-scale problems. The core idea of DDC is to construct the decision graph based on the local density and the distance of each data point, and then select the cluster centers, but the selection of the cluster centers is relatively subjective, and sometimes it is difficult to determine a suitable number of cluster centers. Here, we propose a two-stage clustering algorithm, called DDAP, to overcome these shortcomings. First, we select a small number of potential exemplars based on the two quantities of each data point in DDC to greatly compress the scale of the similarity matrix. Then we implement message-passing on the incomplete similarity matrix. In experiments, two synthetic datasets, nine publicly available datasets, and a real-world electronic medical records (EMRs) dataset are used to evaluate the proposed method. The results demonstrate that DDAP can achieve comparable clustering performance with the original AP algorithm, while the computational efficiency improves observably. INDEX TERMS Exemplar-based clustering, Affinity propagation, Density peaks.

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Clustering with Local Density Peaks-Based Minimum Spanning Tree

Cited by 88 publications

References 41 publications

Fast Incremental Spectral Clustering in Titanate Application via Graph Fourier Transform

Fast Incremental Spectral Clustering in Titanate Application via Graph Fourier Transform

A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks

Fast Clustering by Affinity Propagation Based on Density Peaks

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