Minimum Spanning Tree Based Clustering Algorithms

Grygorash, Oleksandr; Zhou, Yan; Jorgensen, Zach

doi:10.1109/ictai.2006.83

Cited by 153 publications

(76 citation statements)

References 21 publications

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“…This approach requires tuning several constants by hand. More recently, Grygorash et al [9] proposed a hierarchical MST-based clustering approach that iteratively cuts edges, merges points in the resulting components, and rebuilds the spanning tree. We will limit our discussion to the most widely used algorithm from [8].…”

Section: Related Workmentioning

confidence: 99%

Information Theoretic Clustering Using Minimum Spanning Trees

Müller

Nowozin

Lampert

2012

Lecture Notes in Computer Science

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Abstract. In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical foundation it is hard to optimize. We propose an approximate optimization formulation that leads to an efficient algorithm with low runtime complexity. The algorithm has a single free parameter, the number of clusters to find. We demonstrate superior performance on several synthetic and real datasets.

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Section: Related Workmentioning

confidence: 99%

Information Theoretic Clustering Using Minimum Spanning Trees

Müller

Nowozin

Lampert

2012

Lecture Notes in Computer Science

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“…However, corner locations often exhibit clear patterns and can be almost co-linear if they are part of an edge, which means that fast traditional clustering algorithms such as k-means clustering, which tries to find spherical clusters, are not suited for this goal. Instead, a Minimum Spanning Tree (MST) based clustering is used as such solutions are known to be capable of detecting clusters with irregular boundaries [10] and do not need to know the number of clusters in advance.…”

Section: Spatial Clusteringmentioning

confidence: 99%

Sparse optical flow regularization for real-time visual tracking

Spruyt

Ledda

Philips

2013

2013 IEEE International Conference on Multimedia and Expo (ICME)

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Optical flow can greatly improve the robustness of visual tracking algorithms. While dense optical flow algorithms have various applications, they can not be used for real-time solutions without resorting to GPU calculations. Furthermore, most optical flow algorithms fail in challenging lighting environments due to the violation of the brightness constraint. We propose a simple but effective iterative regularisation scheme for real-time, sparse optical flow algorithms, that is shown to be robust to sudden illumination changes and can handle large displacements. The algorithm proves to outperform well known techniques in real life video sequences, while being much faster to calculate. Our solution increases the robustness of a real-time particle filter based tracking application, consuming only a fraction of the available CPU power. Furthermore, a new and realistic optical flow dataset with annotated ground truth is created and made freely available for research purposes.

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“…Grygorash [13] proposed Hierarchical Euclidean distance based MST clustering algorithm (HEMST) and the Maximum Standard Deviation Reduction Clustering Algorithm (MSDR). MSDR does not require any input value for termination.…”

Section: D(xixj)>t Atthmentioning

confidence: 99%

Reducing Runtime Values in Minimum Spanning Tree Based Clustering by Visual Access Tendency

Reddy¹,

Prasad²

2012

IJDKP

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Clustering has been widely used in data analysis. Dissimilarity assesses the distance between objects and this is important in Minimum Spanning Tree (MST) based clustering. An inconsistent edge is identified and removed without knowledge of prior tendency in MST based clustering, which explore the results of clusters in the form of sub-trees. Clustering validity is to be checked at every iterated MST clusters by

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Minimum Spanning Tree Based Clustering Algorithms

Cited by 153 publications

References 21 publications

Information Theoretic Clustering Using Minimum Spanning Trees

Information Theoretic Clustering Using Minimum Spanning Trees

Sparse optical flow regularization for real-time visual tracking

Reducing Runtime Values in Minimum Spanning Tree Based Clustering by Visual Access Tendency

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