scholarly journals The Tessellation-Level-Tree: characterizing the nested hierarchy of density peaks and their spatial distribution in cosmological N-body simulations

2020 ◽  
Vol 493 (4) ◽  
pp. 5693-5712 ◽  
Author(s):  
Philipp Busch ◽  
Simon D M White
Keyword(s):  
Local Density ◽  
Voronoi Tessellation ◽  
Density Peak ◽  
Hierarchical Tree ◽  
Density Peaks ◽  
External Point ◽  
Point Density ◽  
Nested Hierarchy ◽  
Density Threshold ◽  
Hierarchical Tree Structure

ABSTRACT We use the Millennium and Millennium-II simulations to illustrate the Tessellation-Level-Tree  (tlt), a hierarchical tree structure linking density peaks in a field constructed by voronoi tessellation of the particles in a cosmological N-body simulation. The tlt uniquely partitions the simulation particles into disjoint subsets, each associated with a local density peak. Each peak is a subpeak of a unique higher peak. The tlt can be persistence filtered to suppress peaks produced by discreteness noise. Thresholding a peak’s particle list at $\sim 80\left \langle \rho \right \rangle \,$ results in a structure similar to a standard friend-of-friends halo and its subhaloes. For thresholds below $\sim 7\left \langle \rho \right \rangle \,$, the largest structure percolates and is much more massive than other objects. It may be considered as defining the cosmic web. For a threshold of $5\left \langle \rho \right \rangle \,$, it contains about half of all cosmic mass and occupies $\sim 1{{\ \rm per\ cent}}$ of all cosmic volume; a typical external point is then ∼7h−1 Mpc from the web. We investigate the internal structure and clustering of tlt peaks. Defining the saddle point density ρlim  as the density at which a peak joins its parent peak, we show the median value of ρlim  for FoF-like peaks to be similar to the density threshold at percolation. Assembly bias as a function of ρlim  is stronger than for any known internal halo property. For peaks of group mass and below, the lowest quintile in ρlim  has b ≈ 0, and is thus uncorrelated with the mass distribution.

scholarly journals Accelerating Density Peak Clustering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 859 ◽  
Author(s):  
Lin
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Early Stage ◽  
Separation Distance ◽  
Density Peak ◽  
Density Peaks ◽  
Density Based Clustering ◽  
Data Points ◽  
Data Point ◽  
Density Peak Clustering

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to accelerate its computation. On average, the DPC algorithm scans half of the dataset to calculate the separation distance of each data point. We propose an approach to calculate the separation distance of a data point by scanning only the neighbors of the data point. Additionally, the purpose of the separation distance is to assist in choosing the density peaks, which are the data points with both high local density and high separation distance. We propose an approach to identify non-peak data points at an early stage to avoid calculating their separation distances. Our experimental results show that most of the data points in a dataset can benefit from the proposed approaches to accelerate the DPC algorithm.

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Rong Zhou ◽  
Yong Zhang ◽  
Shengzhong Feng ◽  
Nurbol Luktarhan
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Clustering Algorithms ◽  
Complex Structure ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peaks ◽  
Complex Datasets

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 dc 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.

scholarly journals Superpixel Segmentation Based on Grid Point Density Peak Clustering

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6374
Author(s):  
Xianyi Chen ◽  
Xiafu Peng ◽  
Sun’an Wang
Keyword(s):  
Grid Point ◽  
Detection Methods ◽  
Feature Points ◽  
Good Adhesion ◽  
Density Peak ◽  
Superpixel Segmentation ◽  
Density Peaks ◽  
Segmentation Accuracy ◽  
Point Density ◽  
Density Peak Clustering

Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.

Effective and Efficient Browsing of Large Image Databases

2009 ◽  
pp. 142-148
Author(s):  
Gerald Schaefer
Keyword(s):  
Spherical Surface ◽  
Image Databases ◽  
Tree Structure ◽  
Retrieval Method ◽  
Hierarchical Tree ◽  
Large Databases ◽  
The Moment ◽  
Hierarchical Tree Structure ◽  
Large Image Databases ◽  
Interesting Alternative

As image databases are growing, efficient and effective methods for managing such large collections are highly sought after. Content-based approaches have shown large potential in this area as they do not require textual annotation of images. However, while for image databases the query-by-example concept is at the moment the most commonly adopted retrieval method, it is only of limited practical use. Techniques which allow human-centred navigation and visualization of complete image collections therefore provide an interesting alternative. In this chapter we present an effective and efficient approach for user-centred navigation of large image databases. Image thumbnails are projected onto a spherical surface so that images that are visually similar are located close to each other in the visualization space. To avoid overlapping and occlusion effects images are placed on a regular grid structure while large databases are handled through a clustering technique paired with a hierarchical tree structure which allows for intuitive real-time browsing experience.

scholarly journals A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Peijie Lin ◽  
Yaohai Lin ◽  
Zhicong Chen ◽  
Lijun Wu ◽  
Lingchen Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Local Density ◽  
Operating Conditions ◽  
Cluster Center ◽  
Pv System ◽  
Density Peak ◽  
Pv Systems ◽  
Distribution Features ◽  
Clustering Approach ◽  
Distance Vector

Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with6×3 PVarray is established and experimentally tested to investigate the performance of the developed method.

scholarly journals Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1168
Author(s):  
Jun-Lin Lin ◽  
Jen-Chieh Kuo ◽  
Hsing-Wang Chuang
Keyword(s):  
Clustering Algorithm ◽  
Academic Community ◽  
Performance Study ◽  
Potential Density ◽  
Cluster Assignment ◽  
Density Peak ◽  
Single Linkage ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

Word Similarity Algorithm Based on WordNet And HowNet

2012 ◽  
Vol 155-156 ◽  
pp. 375-380 ◽  
Author(s):  
Wu Ling Ren ◽  
Jin Ju Guo
Keyword(s):  
Tree Structure ◽  
Hierarchical Tree ◽  
Word Similarity ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
Precision And Accuracy ◽  
Hierarchical Tree Structure

To make the word similarity calculated results more reasonable and accurate, a new word similarity algorithm is proposed. It uses HowNet primitive hierarchical tree structure, and calculates the two primitives’ distance with the method computing WordNet node distance which considers the tree depth, density, path and connecting intensity, etc. Moreover, algorithm also improves the method that distance into similarity. Finally, this algorithm is compared with related algorithms through experiment. The results show that the proposed algorithm effectively improves the precision and accuracy of word similarity calculation.

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