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Clustering by Search in Descending Order and Automatic Find of Density Peaks
IEEE Access, 2019 Clustering by fast search and find of density peaks published on journal Science in 2014 is a density-based clustering technique, which is not only unnecessary to determine the number of clusters in advance, but also able to recognize the clusters of ...
Tong Liu, Hangyu Li, Xudong Zhao
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Density Peak Clustering Algorithm Based on Shared Neighborhood
Journal of Harbin University of Science and Technology, 2023 To solve the problem of poor clustering performance and automatic determination of cluster center inthe processing of unevenly distributed datasets, a density peak clustering algorithm based on shared neighborhood is proposed ( DPC-SN) .
LI Fuxiang, ZHOU Ming, YANG Tianhao
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Fast multi-image matching via density-based clustering [PDF]
, 2017 We consider the problem of finding consistent matches across multiple images. Previous state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms.
Daniilidis, K. +3 more
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Density-Based Clustering Validation [PDF]
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014 One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters.
Davoud Moulavi +4 more
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Dynamic feature selection for clustering high dimensional data streams [PDF]
, 2019 open access articleChange in a data stream can occur at the concept level and at the feature level. Change at the feature level can occur if new, additional features appear in the stream or if the importance and relevance of a feature changes as the ...
Fahy, Conor, Yang, Shengxiang
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Dynamic Density Based Clustering [PDF]
Proceedings of the 2017 ACM International Conference on Management of Data, 2017 Dynamic clustering---how to efficiently maintain data clusters along with updates in the underlying dataset---is a difficult topic. This is especially true for density-based clustering, where objects are aggregated based on transitivity of proximity, under which deciding the cluster(s) of an object may require the inspection of numerous other objects ...
Junhao Gan, Yufei Tao
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A Novel Density Peaks Clustering Algorithm Based on Local Reachability Density
International Journal of Computational Intelligence Systems, 2020 A novel clustering algorithm named local reachability density peaks clustering (LRDPC) which uses local reachability density to improve the performance of the density peaks clustering algorithm (DPC) is proposed in this paper.
Hanqing Wang +6 more
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A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection [PDF]
, 2016 Peer ...
Huang, Lan +4 more
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Gaussian Mixture Models Algorithm Based on Density Peaks Clustering [PDF]
Jisuanji kexue, 2021 Due to the existence of a large number of sample data which obey the Gaussian distribution,GMM (Gaussian mixture models) is used to cluster these sample data and get more accurate clustering results.In general,EM algorithm(expectation maxi-mization ...
WANG Wei-dong, XU Jin-hui, ZHANG Zhi-feng, YANG Xi-bei
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An efficient density-based clustering algorithm using reverse nearest neighbour [PDF]
, 2018 Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-based clustering algorithm.
A Gionis +24 more
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