scholarly journals A Vibration Method for Discovering Density Varied Clusters

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad T. Elbatta ◽  
Raed M. Bolbol ◽  
Wesam M. Ashour
Keyword(s):  
Local Density ◽  
Density Variation ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Vibration Method ◽  
Correct Number ◽  
The Core ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Different Shapes

DBSCAN is a base algorithm for density-based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it is fail to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper, an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes and sizes that differ in local density. Our proposed method VMDBSCAN first finds out the “core” of each cluster—clusters generated after applying DBSCAN. Then, it “vibrates” points toward the cluster that has the maximum influence on these points. Therefore, our proposed method can find the correct number of clusters.

scholarly journals A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

2017 ◽  
Author(s):  
You Li ◽  
Lin Li ◽  
Dalin Li ◽  
Fan Yang ◽  
Yu Liu
Keyword(s):  
Laser Scanning ◽  
Local Density ◽  
Clustering Method ◽  
Second Stage ◽  
Density Based Clustering ◽  
Processing Step ◽  
Third Stage ◽  
Urban Scene ◽  
Segmentation Problem ◽  
Object Level

The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method.

scholarly journals Quantum algorithm for MMNG-based DBSCAN

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xuming Xie ◽  
Longzhen Duan ◽  
Taorong Qiu ◽  
Junru Li
Keyword(s):  
Domain Knowledge ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Quantum Algorithm ◽  
Neighbor Graph ◽  
Dbscan Algorithm ◽  
Density Based Clustering ◽  
Input Parameters ◽  
Nearest Neighbor Graph

AbstractDBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts-nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart.

Intramitochondrial Viruses of Reptilian Cell Origin

1972 ◽  
Vol 30 ◽  
pp. 318-319
Author(s):  
Philip D. Lunger ◽  
H. Fred Clark
Keyword(s):  
Particle Production ◽  
Outer Zone ◽  
Density Variation ◽  
Core Region ◽  
Mitochondrial Membranes ◽  
Virus Particles ◽  
The Core ◽  
Vipera Russelli ◽  
Maturation Stages ◽  
Type Particle

In the course of fine structure studies of spontaneous “C-type” particle production in a viper (Vipera russelli) spleen cell line, designated VSW, virus particles were frequently observed within mitochondria. The latter were usually enlarged or swollen, compared to virus-free mitochondria, and displayed a considerable degree of cristae disorganization.Intramitochondrial viruses measure 90 to 100 mμ in diameter, and consist of a nucleoid or core region of varying density and measuring approximately 45 mμ in diameter. Nucleoid density variation is presumed to reflect varying degrees of condensation, and hence maturation stages. The core region is surrounded by a less-dense outer zone presumably representing viral capsid.Particles are usually situated in peripheral regions of the mitochondrion. In most instances they appear to be lodged between loosely apposed inner and outer mitochondrial membranes.

scholarly journals A novel bidirectional clustering algorithm based on local density

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baicheng Lyu ◽  
Wenhua Wu ◽  
Zhiqiang Hu
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Algorithms ◽  
Cluster Number ◽  
Denoising Method ◽  
Number Of Clusters ◽  
Data Points ◽  
Cutoff Distance ◽  
Large Clusters ◽  
Small Clusters

AbstractWith the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

A Novel Locality Sensitive K-Means Clustering Algorithm based on Core Clusters

2013 ◽  
Vol 321-324 ◽  
pp. 1939-1942
Author(s):  
Lei Gu
Keyword(s):  
Clustering Algorithm ◽  
Experimental Results ◽  
Clustering Method ◽  
Neighborhood Graph ◽  
The Core ◽  
Random Samples

The locality sensitive k-means clustering method has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random samples are employed for the initial centers. In this paper, an initialization method based on the core clusters is used for the locality sensitive k-means clustering. The core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality sensitive k-means clustering. To investigate the effectiveness of our approach, several experiments are done on three datasets. Experimental results show that our proposed method can improve the clustering performance compared to the previous locality sensitive k-means clustering.

The density variation of the earth's central core*

1942 ◽  
Vol 32 (1) ◽  
pp. 19-29
Author(s):  
K. E. Bullen
Keyword(s):  
Pure Iron ◽  
Outer Boundary ◽  
Density Variation ◽  
Central Core ◽  
Good Precision ◽  
Wide Margin ◽  
The Core ◽  
The Earth ◽  
The Mean ◽  
Published Paper

ABSTRACT A detailed analysis of the problem of the earth's density variation has been extended to the earth's central core. It is shown that in the region between the outer boundary of the core and a distance of about 1400 km. from the earth's center the density ranges from 9.4 gm/cm.3 to 11.5 gm/cm.3 within an uncertainty which, if certain general assumptions are true, does not exceed 3 per cent. The density and pressure figures are, moreover, compatible with the existence of fairly pure iron in this part of the earth. The result for the earth's outer mantle as given in a previously published paper, together with those in the present paper, are found to give with good precision the density distribution in a region occupying 99 per cent of the earth's volume. Values of the density within 1400 km. of the earth's center are subject, however, to a wide margin of uncertainty, and there appears to be no means of resolving this uncertainty for the present. The most that can be said is that the mean density in the latter region is greater than 12.3 gm/cm.3 and may quite possibly be several gm/cm.3 in excess of this figure. In the present paper figures are also included for the variation of gravity and the distribution of pressure within the central core. The gravity results are shown to be subject to an appreciable uncertainty except within about 1000 km. of the outer boundary of the core, but the pressure results are expected to be closely accurate at all depths.

Influence of Xe-135 Dynamic Behavior on Core Operation Safety for a Molten Salt Reactor

2018 ◽  
Author(s):  
Wu Jian-hui ◽  
Li Xiao-xiao ◽  
Hu Ji-feng ◽  
Chen Jin-gen ◽  
Yu Cheng-gang ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Density Variation ◽  
Reactor System ◽  
The Core ◽  
Gas Removal ◽  
Fuel Reactor ◽  
System Functioning ◽  
Neutron Poison ◽  
Neutron Absorption Cross Section ◽  
Removal System

The isotope Xe-135 has a large thermal neutron absorption cross section and is considered to be the most important fission product. A very small amount of such neutron poison may significantly affect the reactor reactivity since they will absorb the neutrons from chain reaction, therefore monitoring their atomic density variation during reactor operation is extremely important. In a molten reactor system, Xe-135 is entrained in the liquid fuel and continuously circulates through the core where the neutron irradiation functions and the external core where only nuclei decay occurs, at the same time, an off-gas removal system operates for online removing Xe-135 through helium bubbling. These unique features of MSR complicate the Xe-135 dynamic behaviors, and the calculation method applied in the solid fuel reactor system is not suitable. From this point, we firstly analytically deduce the nuclei evolution law of Xe-135 in the flowing salt with an off-gas removal system functioning. A study of Xe-135 dynamic behavior with the core power change, shutdown, helium bubbling failure and startup then is conducted, and several valuable conclusions are obtained for MSR design.

scholarly journals TCLUST: Trimming Approach of Robust Clustering Method

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Muhamad Alias Md. Jedi ◽  
Robiah Adnan
Keyword(s):  
Clustering Algorithm ◽  
Likelihood Function ◽  
R Package ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Robust Clustering ◽  
Scatter Matrix ◽  
Group Assignment ◽  
Log Likelihood ◽  
Clustering Approach

TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-means clustering algorithm. It is called “crisp” clustering approach because the observation is can be eliminated or assigned to a group. TCLUST strengthen the group assignment by putting constraint to the cluster scatter matrix. The emphasis in this paper is to restrict on the eigenvalues, λ of the scatter matrix. The idea of imposing constraints is to maximize the log-likelihood function of spurious-outlier model. A review of different robust clustering approach is presented as a comparison to TCLUST methods. This paper will discuss the nature of TCLUST algorithm and how to determine the number of cluster or group properly and measure the strength of group assignment. At the end of this paper, R-package on TCLUST implement the types of scatter restriction, making the algorithm to be more flexible for choosing the number of clusters and the trimming proportion.

scholarly journals Penerapan K-Means Clustering Untuk Seleksi Frame Dominan Berbasis NTSC Pada Obyek Bergerak

2020 ◽  
Vol 7 (4) ◽  
pp. 745
Author(s):  
Rizka Indah Armianti ◽  
Achmad Fanany Onnilita Gaffar ◽  
Arief Bramanto Wicaksono Putra
Keyword(s):  
Feature Extraction ◽  
Video Data ◽  
Clustering Method ◽  
Data Frame ◽  
First Order ◽  
Feature Extraction And Selection ◽  
Different Shapes ◽  
Frame Set ◽  
Selection Of

<p class="Abstrak">Obyek dinyatakan bergerak jika terjadi perubahan posisi dimensi disetiap <em>frame</em>. Pergerakan obyek menyebabkan obyek memiliki perbedaan bentuk pola disetiap <em>frame-</em>nya. <em>Frame</em> yang memiliki pola terbaik diantara <em>frame</em> lainnya disebut <em>frame</em> dominan. Penelitian ini bertujuan untuk menyeleksi <em>frame</em> dominan dari rangkaian <em>frame</em> dengan menerapkan metode K-means <em>clustering</em> untuk memperoleh <em>centroid</em> dominan (<em>centroid</em> dengan nilai tertinggi) yang digunakan sebagai dasar seleksi <em>frame</em> dominan. Dalam menyeleksi <em>frame</em> dominan terdapat 4 tahapan utama yaitu akuisisi data, penetapan pola obyek, ekstrasi ciri dan seleksi. Data yang digunakan berupa data video yang kemudian dilakukan proses penetapan pola obyek menggunakan operasi pengolahan citra digital, dengan hasil proses berupa pola obyek RGB yang kemudian dilakukan ekstraksi ciri berbasis NTSC dengan menggunakan metode statistik orde pertama yaitu <em>Mean</em>. Data hasil ekstraksi ciri berjumlah 93 data <em>frame</em> yang selanjutnya dikelompokkan menjadi 3 <em>cluster</em> menggunakan metode K-Means. Dari hasil <em>clustering</em>, <em>centroid</em> dominan terletak pada <em>cluster</em> 3 dengan nilai <em>centroid</em> 0.0177 dan terdiri dari 41 data <em>frame</em>. Selanjutnya diukur jarak kedekatan seluruh data <em>cluster</em> 3 terhadap <em>centroid</em>, data yang memiliki jarak terdekat dengan <em>centroid</em> itulah <em>frame</em> dominan. Hasil seleksi <em>frame</em> dominan ditunjukkan pada jarak antar <em>centroid</em> dengan anggota <em>cluster</em>, dimana dari seluruh 41 data frame tiga jarak terbaik diperoleh adalah 0.0008 dan dua jarak bernilai  0.0010 yang dimiliki oleh <em>frame</em> ke-59, ke-36 dan ke-35.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The object is declared moving if there is a change in the position of the dimensions in each frame. The movement of an object causes the object to have different shapes in each frame. The frame that has the best pattern among other frames is called the dominant frame. This study aims to select the dominant frame from the frame set by applying the K-means clustering method to obtain the dominant centroid (the highest value centroid) which is used as the basis for the selection of dominant frames. In selecting dominant frames, there are 4 main stages, namely data acquisition, determination of object patterns, feature extraction and selection. The data used in the form of video data which is then carried out the process of determining the pattern of objects using digital image processing operations, with the results of the process in the form of an RGB object pattern which is then performed NTSC-based feature extraction using the first-order statistical method, Mean. The data from feature extraction are 93 data frames which are then grouped into 3 clusters using the K-Means method. From the results of clustering, the dominant centroid is located in cluster 3 with a centroid value of 0.0177 and consists of 41 data frames. Furthermore, the proximity of all data cluster 3 to the centroid is measured, the data having the closest distance to the centroid is the dominant frame. The results of dominant frame selection are shown in the distance between centroids and cluster members, where from all 41 data frames the three best distances obtained are 0.0008, 0.0010, and 0.0010 owned by 59th, 36th and 35th frames.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p><p> </p>

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