Consensus Clustering Using kNN Mode Seeking

Myhre, Jonas Nordhaug; Mikalsen, Karl Øyvind; Løkse, Sigurd; Jenssen, Robert

doi:10.1007/978-3-319-19665-7_15

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…• Spectral clustering techniques could be used in the final step [66,1,67] • Quick Shift or medioid shift could replace kNN mode seeking [68,69].…”

Section: Discussionmentioning

confidence: 99%

“…Our experience is that the proposed algorithm works for a wide range of parameters. However, for all experiments we used the same parameters, inspired by and extended from the previous work [1], where we used a fixed range of neighborhood parameters to capture cluster structures on different number of scales. This showed promising results, and in this work we use a slightly modified range when sampling the k parameters used in the proposed algorithm:…”

Section: Methodsmentioning

confidence: 99%

“…This work is an extended version of[1], presented at the Scandinavian Conference on Image Analysis 2015.…”

mentioning

confidence: 99%

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Robust clustering using a kNN mode seeking ensemble

Myhre

Mikalsen

Lkse

et al. 2018

Pattern Recognition

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In this paper we present a new algorithm for parameter-free clustering by mode seeking. Mode seeking, especially in the form of the mean shift algorithm, is a widely used strategy for clustering data, but at the same time prone to poor performance if the parameters are not chosen correctly. We propose to form a clustering ensemble consisting of repeated and bootstrapped runs of the recent kNN mode seeking algorithm, an algorithm which is faster than ordinary mean shift and more suited for high dimensional data. This creates a robust mode seeking clustering algorithm with respect to the choice of hyper-parameters and high dimensional input spaces, while at the same inheriting all other strengths of mode seeking in general. We demonstrate promising results on a number of synthetic and real data sets.

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“…• Spectral clustering techniques could be used in the final step [66,1,67] • Quick Shift or medioid shift could replace kNN mode seeking [68,69].…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Robust clustering using a kNN mode seeking ensemble

Myhre

Mikalsen

Lkse

et al. 2018

Pattern Recognition

Self Cite

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“…For this reason it is important that the clustering method is robust and not sensitive to parameter choices. We therefore use the kNN mode seeking consensus clustering algorithm [16] (Appendix A), which has been shown to be robust on a variety of datasets. The idea with the clustering is to identify groups of patients of different health status.…”

Section: Predictive Anchors Via Exploratory Analysismentioning

confidence: 99%

Using anchors from free text in electronic health records to diagnose postoperative delirium

Mikalsen

Soguero-Ruíz

Jensen

et al. 2017

Computer Methods and Programs in Biomedicine

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Objectives. Postoperative delirium is a common complication after major surgery among the elderly. Despite its potentially serious consequences, the complication often goes undetected and undiagnosed. In order to provide diagnosis support one could potentially exploit the information hidden in free text documents from electronic health records using data-driven clinical decision support tools. However, these tools depend on labeled training data and can be both time consuming and expensive to create. Methods. The recent "Learning with Anchors" framework resolves this problem by transforming key observations (anchors) into labels. This is a promising framework, but it is heavily reliant on clinician's knowledge for specifying good anchor choices in order to perform well. In this paper we propose a novel method for specifying anchors from free text documents, following an exploratory data analysis approach based on clustering and data visualization techniques. We investigate the use of the new framework as a way to detect postoperative delirium. Results. By applying the proposed method to medical data gathered from a Norwegian University Hospital, we increase the area under the precision-recall curve from 0.51 to 0.96 compared to baselines. Conclusions. The proposed approach can be used as a framework for clinical decision support for postoperative delirium.

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“…Given a critical point 2 m, r(m) = 0, all points that have integral curves that converges to the critical point are said to lie in the basin of attraction of m [43]. This property is the foundation of mode based clustering algorithms, e.g mean shift clustering [44,45].…”

Section: Properties Of Density Ridgesmentioning

confidence: 99%

Manifold learning and unwrapping using density ridges

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xii 3.6 Unwrapped mnist-0 digits with a selection of images of the actual digits on top. Orientation and thickness of digits smoothly vary across horizontal and vertical directions, respectively. . 34 3.7 Unfolding mnist-0 digits using two benchmark manifold learning methods. Unlike the proposed method, no smooth variation of structure is preserved using these methods. . . . . 35 3.8 Unfolding mnist-2 digits using the proposed method and t-SNE. Similar to Figure 3.7(b) for mnist-0, no smooth variation of structure is preserved using t-SNE, while proposed method shows smooth change of thickness and shape in horizontal and vertical coordinates, respectively. 36 3.9 Unfolding mnist-9 digits using the proposed method, which shows smooth change of thickness Sylvain Bouix, who were also my mentors, for all of their guidance through this process; your discussion, ideas, and feedback have been absolutely invaluable.

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Consensus Clustering Using kNN Mode Seeking

Cited by 4 publications

References 19 publications

Robust clustering using a kNN mode seeking ensemble

Robust clustering using a kNN mode seeking ensemble

Using anchors from free text in electronic health records to diagnose postoperative delirium

Manifold learning and unwrapping using density ridges

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