Robust path-based spectral clustering

Chang, Hong; Yeung, Dit–Yan

doi:10.1016/j.patcog.2007.04.010

Cited by 499 publications

(234 citation statements)

References 13 publications

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“…Artificial datasets 1, 2, and 3 are selected. Dataset 1 is from [32], which consists of clusters with both convex and nonconvex shapes in a hierarchical structure; dataset 2 [33] consists of 3 nonconvex shape clusters; dataset 3 is a synthetic dataset consisting of 2 isotropic Gaussian blobs and 2 interleaving half-circles. By choosing an appropriate k ∈ 3, 7 , the intuitive visualization of hierarchical structure and clustering results is shown in Figure 1.…”

Section: Experiments Results and Discussionmentioning

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: Experiments Results and Discussionmentioning

confidence: 99%

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

et al. 2018

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“…We created an interlocked spirals dataset, shown in Fig. 2, which is considered to be a challenging benchmark for spectral clustering [22]. To alleviate this challenging clustering Fig.…”

Section: Results Of Comparison To Kernel-pcamentioning

confidence: 99%

“…Segmentation of asymmetric data with binary k-means in SC (a) and SCE (c) constructed feature spaces. An interlocked two spirals dataset is considered as a challenging benchmark for SC [22]. As can be seen in (c), SCE results in a better intercluster separation in the feature space.…”

Section: Results Of Comparison To Kernel-pcamentioning

confidence: 99%

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A New Anticorrelation-Based Spectral Clustering Formulation

Dietlmeier

Ghita

Whelan

2011

Advanced Concepts for Intelligent Vision Systems

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Abstract. This paper introduces the Spectral Clustering Equivalence (SCE) algorithm which is intended to be an alternative to spectral clustering (SC) with the objective to improve both speed and quality of segmentation. Instead of solving for the spectral decomposition of a similarity matrix as in SC, SCE converts the similarity matrix to a columncentered dissimilarity matrix and searches for a pair of the most anticorrelated columns. The orthogonal complement to these columns is then used to create an output feature vector (analogous to eigenvectors obtained via SC), which is used to partition the data into discrete clusters. We demonstrate the performance of SCE on a number of artificial and real datasets by comparing its classification and image segmentation results with those returned by kernel-PCA and Normalized Cuts algorithm. The column-wise processing allows the applicability of SCE to Very Large Scale problems and asymmetric datasets.

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“…A comprehensive review can be found in the papers [3], [4]. Unlike classical partitioning clustering algorithms, spectral clustering produces better clustering results on the data sets with highly nonlinear and elongated clusters [3], [5], such as circle or stick distribution. There are some well-known spectral clustering algorithms, such as Shi and Malik algorithm [1], Ng, Jordan and Weiss algorithm (NJW) [2], and others [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Clustering Algorithm for Irregularly Shaped Clusters

Tang

Zhu

Cao

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYTo detect the natural clusters for irregularly shaped data distribution is a difficult task in pattern recognition. In this study, we propose an efficient clustering algorithm for irregularly shaped clusters based on the advantages of spectral clustering and Affinity Propagation (AP) algorithm. We give a new similarity measure based on neighborhood dispersion analysis. The proposed algorithm is a simple but effective method. The experimental results on several data sets show that the algorithm can detect the natural clusters of input data sets, and the clustering results agree well with that of human judgment.

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Robust path-based spectral clustering

Cited by 499 publications

References 13 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

A New Anticorrelation-Based Spectral Clustering Formulation

An Efficient Clustering Algorithm for Irregularly Shaped Clusters

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