scholarly journals A spectral clustering method of taking the space topology relationship and the voltage data dependency of the power network into consideration

2017 ◽  
Keyword(s):  
Spectral Clustering ◽  
Data Dependency ◽  
Power Network ◽  
Clustering Method ◽  
Space Topology ◽  
Spectral Clustering Method

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

Identifying similar functional modules by a new hybrid spectral clustering method

2012 ◽  
Vol 6 (5) ◽  
pp. 175-186 ◽  
Author(s):  
S. Madani ◽  
M. Aminghafari ◽  
K. Faez
Keyword(s):  
Spectral Clustering ◽  
Functional Modules ◽  
Clustering Method ◽  
Spectral Clustering Method

scholarly journals Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

Fluids ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 184
Author(s):  
Guilherme S. Vieira ◽  
Irina I. Rypina ◽  
Michael R. Allshouse
Keyword(s):  
Spectral Clustering ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Material Transport ◽  
Ensemble Forecasts ◽  
Uncertainty Model ◽  
Ocean Flows ◽  
Ocean Forecasting ◽  
High Uncertainty ◽  
Spectral Clustering Method

Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced spanning a range of potential outcomes, and trajectory clustering is used to identify robust features and quantify the uncertainty of the ensemble-averaged results. First, ensemble statistics are used to investigate the cluster sensitivity to the spectral clustering method free-parameters and the forecast parameters for the analytic Bickley jet, a geostrophic flow model. Then, we analyze an operational coastal ocean ensemble forecast and compare the clustering results to drifter trajectories south of Martha’s Vineyard. This approach identifies regions of low uncertainty where drifters released within a cluster predominantly remain there throughout the window of analysis. Drifters released in regions of high uncertainty tend to either enter neighboring clusters or deviate from all predicted outcomes.

scholarly journals Analisis Clustering Virus MERS-CoV Menggunakan Metode Spectral Clustering Dan Algoritma K-Means

2021 ◽  
Vol 5 (3) ◽  
pp. 315
Author(s):  
Septian Wulandari ◽  
Dian Novita
Keyword(s):  
Dna Sequences ◽  
Spectral Clustering ◽  
Complex Structure ◽  
World Health ◽  
Acute Respiratory Disease ◽  
Clustering Method ◽  
Grouping Method ◽  
Quantitative Descriptive ◽  
Partition Clustering ◽  
Spectral Clustering Method

<p><em>The MERS-Cov virus has spread to other countries outside Saudi Arabia. This is because the MERS-CoV virus can mutate rapidly so it is feared that it could threaten public health and even world health. This virus develops and becomes an acute respiratory disease and the mortality rate reaches 30% among 536 cases. One way to classify the MERS-CoV virus is by grouping the DNA sequences of the MERS-CoV virus which have similar characteristics and functions. Spectral clustering is a grouping method that can identify DNA gene expression. This method is also able to partition DNA data with a more complex structure than the partition clustering method. The purpose of this study was to analyze the MERS-CoV virus clustering using the spectral clustering method and the k-means algorithm. This study used a quantitative descriptive literature approach. The results showed that the results of clustering using the spectral clustering method and the k-means algorithm produced three clusters and were more homogeneous than clustering using k-means only.</em></p>

A signal-diffusion-based spectral clustering method for community detection

2019 ◽  
Vol 18 (01) ◽  
pp. 1941019
Author(s):  
Zheng Qiong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Adjacency Matrix ◽  
Spectral Clustering ◽  
Time Complexity ◽  
Detection Method ◽  
Signal Transmission ◽  
Transmission Mechanism ◽  
Clustering Method ◽  
Spectral Clustering Method

As the traditional spectral community detection method uses adjacency matrix for clustering which might cause the problem of accuracy reduction, we proposed a signal-diffusion-based spectral clustering for community detection. This method solves the problem that unfixed total signal as using the signal transmission mechanism, provides optimization of algorithm time complexity, improves the performance of spectral clustering with construction of Laplacian based on signal diffusion. Experiments prove that the method reaches as better performance on real-world network and Lancichinetti–Fortunato–Radicchi (LFR) benchmark.

A Novel Low Rank Spectral Clustering Method for Face Identification

2019 ◽  
Vol 13 (4) ◽  
pp. 387-394
Author(s):  
Danhua Xu ◽  
Chuan Li ◽  
Teng Chen ◽  
Fei Lang
Keyword(s):  
Spectral Clustering ◽  
Low Rank ◽  
Superior Performance ◽  
Face Identification ◽  
Identification Algorithm ◽  
Clustering Method ◽  
Optimization Framework ◽  
Learned Dictionary ◽  
The Face ◽  
Spectral Clustering Method

<P>Background: Low rank is a recent significant model to explore the inner subspace structure of samples, which has been successfully used in many pattern recognition tasks. </P><P> Methods: In this paper, we proposed a novel spectral clustering method to address the face identification problem. There are three main contributions in our paper. Firstly, the sparse coding under a cluster-based learned dictionary is taken as the character sample of each face. Secondly, the collaborative low rank representation is incorporated in the comprehensive optimization framework to construct an effective affinity graph iteratively, which is different from the conventional ones to tackle the graph construction and spectral clustering independently. We revised all the patents relating to the face identification. Thirdly, a numerical algorithm is developed to solve the optimization framework and obtain a stable solution. </P><P> Results: The experimental results showed the superior performance of the proposed method on recognition ratio. </P><P> Conclusion: It means that our proposed low rank based identification algorithm outperforms the existed excellent methods.</P>

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