Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors

Xie, Juanying; Gao, Hongchao; Xie, Weixin; Liu, Xiaohui; Grant, P.W.

doi:10.1016/j.ins.2016.03.011

Cited by 309 publications

(161 citation statements)

References 18 publications

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“…The cluster centers are determined by a residual analysis. Reference [5] computed local density of each point using its k-nearest neighbors instead of d c in a kernel density estimation. Reference [6] also adopted a new local density metric using k-nearest neighbors in a kernel density estimation too.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

et al. 2018

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Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently find them. The clustering by fast search and find of density peak algorithm is proposed to intuitively determine cluster centers and assign points to corresponding partitions for complex datasets. This method incorporates simple structure due to the noniterative logic and less few parameters; however, the guidelines for parameter selection and center determination are not explicit. To tackle these problems, we propose an improved hierarchical clustering method HCDP aiming to represent the complex structure of the dataset. A k-nearest neighbor strategy is integrated to compute the local density of each point, avoiding to select the nonnecessary global parameter d c and enables cluster smoothing and condensing. In addition, a new clustering evaluation approach is also introduced to extract a "flat" and "optimal" partition solution from the structure by adaptively computing the clustering stability. The proposed approach is conducted on some applications with complex datasets, where the results demonstrate that the novel method outperforms its counterparts to a large extent.

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Section: Related Workmentioning

confidence: 99%

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

et al. 2018

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“…This fast clustering algorithm assumes that cluster centers are surrounded by neighbors with lower local densities; meanwhile, they are at a relatively large distance from the points with a higher local density [16,17]. There are two ways to calculate local density, including cut-off kernel and Gaussian kernel.…”

Section: Fast Clustering Algorithmmentioning

confidence: 99%

“…Therefore, we proposed an approach based on a fast clustering algorithm to reduce the number of the majority data from the imbalanced data. This fast clustering algorithm was proposed by Rodriguez and Laio in 2014 based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities [14][15][16][17]. Based on these two assumptions, the fast clustering algorithm can be used to dispose of different clusters.…”

Section: Introductionmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

et al. 2017

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To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

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“…However, this reduction process can cause a loss of information that leads to prefer the development of ad-hoc clustering techniques that incorporate spatial as well as temporal information. Similar to the classification proposed by Fouedjio (2016) for the clustering of spatial data, existing spatial-time clustering models can be distinguished into the following four different approaches: non-spatial time series clustering based on a spatial dissimilarity measure (Izakian et al, 2013); spatially constrained time series clustering (Hu & Sung, 2006;Coppi et al, 2010;Gao & Yu, 2016); density-based clustering (Ester et al, 1996;Wang et al, 2006;Birant & Kut, 2007;Ienco & Bordogna, 2016;Xie et al, 2016); model-based clustering (Basford & McLachlan, 1985;Viroli, 2011;Torabi, 2014Torabi, , 2016.…”

Section: Introductionmentioning

confidence: 99%

Copula-based fuzzy clustering of spatial time series

2017

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This paper contributes to the existing literature on the analysis of spatial time series presenting a new clustering algorithm called COFUST, i.e. COpula-based FUzzy clustering algorithm for Spatial Time series. The underlying idea of this algorithm is to perform a fuzzy Partitioning Around Medoids (PAM) clustering using copula-based approach to interpret comovements of time series. This generalisation allows both to extend usual clustering methods for time series based on Pearson's correlation and to capture the uncertainty that arises assigning units to clusters. Furthermore, its flexibility permits to include directly in the algorithm the spatial information. Our approach is presented and discussed using both simulated and real data, highlighting its main advantages.

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Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors

Cited by 309 publications

References 18 publications

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

Copula-based fuzzy clustering of spatial time series

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