CutESC: Cutting edge spatial clustering technique based on proximity graphs

Aksaç, Alper; Özyer, Tansel; Alhajj, Reda

doi:10.1016/j.patcog.2019.06.014

Cited by 18 publications

(19 citation statements)

References 39 publications

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“…The sky gradients mostly caused over-segmentations, which were not properly addressed by any strategy. Additionally, we also included results from purely clustering algorithms, such as the MeanShift (MS) algorithm or the newer Cutting edge spatial clustering (CutESC) [41], which exhibited good results in their study. We have used scikit-learn Python MS implementation [42] with default values.…”

Section: Image-segmentation Tasksmentioning

confidence: 99%

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

2020

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A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

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Section: Image-segmentation Tasksmentioning

confidence: 99%

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

2020

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“…This article provides details about a novel algorithm (CutESC) for spatial clustering based on proximity graphs introduced in Ref. [1]. Moreover, the data in this article describes tables and figures in support of the article titled “CutESC: Cutting edge spatial clustering technique based on proximity graphs” [1].…”

Section: Datamentioning

confidence: 94%

“…[1]. Moreover, the data in this article describes tables and figures in support of the article titled “CutESC: Cutting edge spatial clustering technique based on proximity graphs” [1]. CutESC performs clustering automatically for non-uniform densities, arbitrary shapes, and outliers without requiring any prior information and preliminary parameters.…”

Section: Datamentioning

confidence: 99%

Data on cut-edge for spatial clustering based on proximity graphs

2020

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Cluster analysis plays a significant role regarding automating such a knowledge discovery process in spatial data mining. A good clustering algorithm supports two essential conditions, namely high intra-cluster similarity and low inter-cluster similarity. Maximized intra-cluster/within-cluster similarity produces low distances between data points inside the same cluster. However, minimized inter-cluster/between-cluster similarity increases the distance between data points in different clusters by furthering them apart from each other. We previously presented a spatial clustering algorithm, abbreviated CutESC (Cut-Edge for Spatial Clustering) with a graph-based approach. The data presented in this article is related to and supportive to the research paper entitled “CutESC: Cutting edge spatial clustering technique based on proximity graphs” (Aksac et al., 2019) [1], where interpretation research data presented here is available. In this article, we share the parametric version of our algorithm named CutESC-P, the best parameter settings for the experiments, the additional analyses and some additional information related to the proposed algorithm (CutESC) in [1].

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“…This is another way to overcome subjectivity in decisionmaking. A breast cancer and its grade can be detected more accurately by combining machine learning and graph theory algorithms [7,8] with image analysis.…”

Section: Introductionmentioning

confidence: 99%

CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology

et al. 2020

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Objective: Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates. Results: CACTUS has been evaluated using a surgical pathology application-the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.

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CutESC: Cutting edge spatial clustering technique based on proximity graphs

Cited by 18 publications

References 39 publications

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Data on cut-edge for spatial clustering based on proximity graphs

CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology

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