On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset

Srivastava, Amber; Baranwal, Mayank; Salapaka, Srinivasa M.

doi:10.1609/aaai.v33i01.33015000

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Performance indexes including SA, mean intersection-overunion (mIoU) and E-measure [30][31][32] are used to give numerical results of SRG, ISRG, JSEG, algorithm in [13], algorithm in [17] and the proposed algorithm. SA is defined as the ratio of a number of correctly classified pixels to the total number of all pixels.…”

Section: Experiments On Region Growing Based Algorithmsmentioning

confidence: 99%

Salient region growing based on Gaussian pyramid

Jiao

Wang

Zhang

et al. 2021

IET Image Processing

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For region growing image segmentation, seed selection and image noise are two major concerns causing negative segmentation performance. This paper proposes a regional growth algorithm based on the Gaussian pyramid (GPRG), which automatically selects seed points and optimizes the growth path. To achieve automatic seed selection, Gaussian difference pyramid generated by the Gaussian pyramid is required. The growing seed of the salient target in an image is acquired by searching for extremums in each image of the Gaussian difference pyramid. For growing path optimization, edge changing direction is updated according to different Gaussian difference pyramid images with sizes from small to large. The edge of region growing is determined by the edge of the salient target in an image. Segmentation experiments show that this algorithm is able to automatically select seeds and accurately segment the salient target in an image with and without image noise. Performances in terms of segmentation accuracy (SA), mean intersection-over-union (mIoU) and E-measure are superior compared with other methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Experiments On Region Growing Based Algorithmsmentioning

confidence: 99%

Salient region growing based on Gaussian pyramid

Jiao

Wang

Zhang

et al. 2021

IET Image Processing

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show abstract

“…The number of edges needed to reach maximum correlation depends on the size and nature of the data. Intuitively, the association between D X and D U is related to the concept of persistence of clustering solutions [35] , i.e., if the shape is persistent along the edges, the computed correlation will be consistently similar. Fig.…”

Section: Optional: Visualize the Relationship Between Correlation And The Number Of Edgesmentioning

confidence: 99%

Spanning Trees as Approximation of Data Structures

Alcaide

Aerts

2021

IEEE Trans. Visual. Comput. Graphics

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The connections in a graph generate a structure that is independent of a coordinate system. This visual metaphor allows creating a more flexible representation of data than a two-dimensional scatterplot. In this work, we present STAD (Simplified Topological Abstraction of Data), a parameter-free dimensionality reduction method that projects high-dimensional data into a graph. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the approximate distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the distances from the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of additional functions to focus the exploration and allow the analysis of data from new perspectives, emphasizing traits in data which otherwise would remain hidden. We demonstrate the effectiveness of our method by applying it to two real-world datasets: traffic density in Barcelona and temporal measurements of air quality in Castile and León in Spain.

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“…The number of edges needed to reach maximum correlation depends on the size and nature of the data. Intuitively, the association between D X and D U is related to the concept of persistence of clustering solutions [34] , i.e., if the shape is persistent along the edges, the computed correlation will be consistently similar. Fig.…”

Section: Optional: Visualize the Relationship Between Correlation And...mentioning

confidence: 99%

Improving the Projection of Global Structures in Data through Spanning Trees

Alcaide¹,

Aerts²

2019

Preprint

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The connection of edges in a graph generates a structure that is independent of a coordinate system. This visual metaphor allows creating a more flexible representation of data than a two-dimensional scatterplot. In this work, we present STAD (Spanning Trees as Approximation of Data), a dimensionality reduction method to approximate the high-dimensional structure into a graph with or without formulating prior hypotheses. STAD generates an abstract representation of high-dimensional data by giving each data point a location in a graph which preserves the distances in the original high-dimensional space. The STAD graph is built upon the Minimum Spanning Tree (MST) to which new edges are added until the correlation between the distances from the graph and the original dataset is maximized. Additionally, STAD supports the inclusion of additional functions to focus the exploration and allow the analysis of data from new perspectives, emphasizing traits in data which otherwise would remain hidden. We demonstrate the effectiveness of our method by applying it to two real-world datasets: traffic density in Barcelona and temporal measurements of air quality in Castile and Le ón in Spain.

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On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset

Cited by 5 publications

References 22 publications

Salient region growing based on Gaussian pyramid

Salient region growing based on Gaussian pyramid

Spanning Trees as Approximation of Data Structures

Improving the Projection of Global Structures in Data through Spanning Trees

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