scholarly journals Spectral Clustering of Psychological Networks

2021 ◽  
Author(s):  
Michael Brusco ◽  
Douglas Steinley ◽  
Ashley L. Watts
Keyword(s):  
Spectral Clustering ◽  
Cluster Structure ◽  
Laplacian Matrix ◽  
Simulation Experiments ◽  
Number Of Clusters ◽  
Laplacian Matrices ◽  
Undirected Network ◽  
Close Relationship ◽  
Recovery Performance ◽  
Better Than

Spectral clustering is a well-known method for clustering the vertices of an undirected network. Although its use in network psychometrics has been limited, spectral clustering has a close relationship to the commonly-used walktrap algorithm. In this paper, we report results from four simulation experiments designed to evaluate the ability of spectral clustering and the walktrap algorithm to recover underlying cluster structure in networks. The salient findings include: (1) the cluster recovery performance of the walktrap algorithm can be improved slightly by using exact K-means clustering instead of hierarchical clustering; (2) K-means and K-median clustering led to comparable recovery performance when used to cluster vertices based on the eigenvectors of Laplacian matrices in spectral clustering; (3) spectral clustering using the unnormalized Laplacian matrix generally yielded inferior cluster recovery in comparison to the other methods; (4) when the correct number of clusters was provided for the methods, spectral clustering using the normalized Laplacian matrix led to better recovery than the walktrap algorithm; (5) when the number of clusters was unknown, spectral clustering using the normalized Laplacian matrix was appreciably better than the walktrap algorithm when the clusters were equally-sized, but the two methods were competitive when the clusters were not equally-sized. Overall, both the walktrap algorithm and spectral clustering of the normalized Laplacian matrix are effective for partitioning the vertices of undirected networks, with the latter performing better in most instances.

scholarly journals Multi-View Spectral Clustering with Optimal Neighborhood Laplacian Matrix

2020 ◽  
Vol 34 (04) ◽  
pp. 6965-6972
Author(s):  
Sihang Zhou ◽  
Xinwang Liu ◽  
Jiyuan Liu ◽  
Xifeng Guo ◽  
Yawei Zhao ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Optimization Problem ◽  
State Of The Art ◽  
Laplacian Matrix ◽  
High Order ◽  
Insufficient Information ◽  
Complementary Information ◽  
First Order ◽  
Laplacian Matrices ◽  
Group Data

Multi-view spectral clustering aims to group data into different categories by optimally exploring complementary information from multiple Laplacian matrices. However, existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct an optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. In this paper, we propose a novel optimal neighborhood multi-view spectral clustering (ONMSC) algorithm to address these issues. Specifically, the proposed algorithm generates an optimal Laplacian matrix by searching the neighborhood of both the linear combination of the first-order and high-order base Laplacian matrices simultaneously. This design enhances the representative capacity of the optimal Laplacian and better utilizes the hidden high-order connection information, leading to improved clustering performance. An efficient algorithm with proved convergence is designed to solve the resultant optimization problem. Extensive experimental results on 9 datasets demonstrate the superiority of our algorithm against state-of-the-art methods, which verifies the effectiveness and advantages of the proposed ONMSC.

scholarly journals The Application of Baum-Welch Algorithm in Multistep Attack

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yanxue Zhang ◽  
Dongmei Zhao ◽  
Jinxing Liu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Simulation Experiments ◽  
Forward Algorithm ◽  
Better Than

The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.

The Development of Wavelet Transform and its Application in Image Denoise

2013 ◽  
Vol 694-697 ◽  
pp. 2003-2008
Author(s):  
Ming Hong Dai
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Filter Bank ◽  
Experimental Studies ◽  
Visual Quality ◽  
Simulation Experiments ◽  
Design And Implementation ◽  
Direction Filter ◽  
Related Design ◽  
Better Than

The paper introduces Laplace pyramid, Ridgelet and Curvelet principle, structure and methods, and their denoising experimental studies. It also introduces the traditional direction filter of principle, structure and methodology, and the simulation experiments show that its image denoising PSNR is slightly lower than wavelet but denoising image visual quality is better than former. To that end, proposed a new direction filters that uniform direction filter banks and non-uniform direction filters, proved filter passband condition and related design and implementation issues were discussed. nonlinear experiment shows that the new direction filter bank was better than the wavelet.

The Research of Heuristic Algorithm for Flow Shop Scheduling Problem

2012 ◽  
Vol 605-607 ◽  
pp. 528-531
Author(s):  
Dan Tang ◽  
Hong Ping Shu
Keyword(s):  
Heuristic Algorithm ◽  
Processing Time ◽  
Flow Shop ◽  
Heuristic Method ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Average Quality ◽  
Simulation Experiments ◽  
Shop Scheduling ◽  
Better Than

For the flow shop scheduling problem which aims to minimize makespan, this paper gives a new derivation about its mathematical definition, and mining characteristics of the problem itself further. By which analysis, the new heuristic method proposed in the paper shorten the waiting time of each job as much as possible on the basis of reduce the processing time of the first machine and last job. The result of simulation experiments shows that, our new heuristic algorithm has good performance, and the average quality and stability of scheduling sequences generated by new method is significantly better than other heuristic algorithm which has the same complexity.

scholarly journals Global spectral clustering in dynamic networks

2018 ◽  
Vol 115 (5) ◽  
pp. 927-932 ◽  
Author(s):  
Fuchen Liu ◽  
David Choi ◽  
Lu Xie ◽  
Kathryn Roeder
Keyword(s):  
Community Detection ◽  
Spectral Clustering ◽  
Signal To Noise Ratio ◽  
Dynamic Networks ◽  
Brain Regions ◽  
Tuning Parameter ◽  
Expression Data ◽  
Dynamic Community Detection ◽  
Dynamic Community ◽  
Better Than

Community detection is challenging when the network structure is estimated with uncertainty. Dynamic networks present additional challenges but also add information across time periods. We propose a global community detection method, persistent communities by eigenvector smoothing (PisCES), that combines information across a series of networks, longitudinally, to strengthen the inference for each period. Our method is derived from evolutionary spectral clustering and degree correction methods. Data-driven solutions to the problem of tuning parameter selection are provided. In simulations we find that PisCES performs better than competing methods designed for a low signal-to-noise ratio. Recently obtained gene expression data from rhesus monkey brains provide samples from finely partitioned brain regions over a broad time span including pre- and postnatal periods. Of interest is how gene communities develop over space and time; however, once the data are divided into homogeneous spatial and temporal periods, sample sizes are very small, making inference quite challenging. Applying PisCES to medial prefrontal cortex in monkey rhesus brains from near conception to adulthood reveals dense communities that persist, merge, and diverge over time and others that are loosely organized and short lived, illustrating how dynamic community detection can yield interesting insights into processes such as brain development.

Nationalism

2010 ◽  
Author(s):  
Peter Baldwin
Keyword(s):  
Industrial Complex ◽  
Advertising Agencies ◽  
Military Industrial Complex ◽  
The World ◽  
Close Relationship ◽  
Selling Point ◽  
Better Than ◽  
Made In

Americans Are Patriotic And Nationalist, but not more than some Europeans (figure 173). Unsurprisingly, Germans are least proud of their nation, and rather unexpectedly and cheerily, the Portuguese—not the Americans—are most proud, with the Irish tied for second place. A 2007 survey reveals that a larger proportion of Italians consider their culture superior than any other nationalities surveyed, including the Americans. Another survey finds that only the Irish feel more uniformly proud to be of their nation. Proportionately more Austrians, Irish, French, and Danes claim they feel very close to their nation than do Americans. Americans are more likely than any Europeans to think that their country is better than most others. But proportionately more Portuguese, Danes, and Spaniards feel that the world would be improved if other people were like them. And any U.S. tendency to boosterism is tempered by the finding that a larger fraction of Americans admits that certain aspects of their country shame them than do the Germans, Austrians, Spanish, French, Danes, or Finns. No country more robustly projects its own nationalist aspirations in the products it sells abroad than the supposedly postnational Swedes. Swedish manufacturers, or at least their advertising agencies, seem convinced that the sheer fact of being Swedish is a selling point. Ikea’s walls are adorned with musings on the preternaturally close relationship between Swedes and nature that allegedly sets them apart from the rest of humanity, as are packets of Wasa crispbread. Asko’s slogan, “Made In Sweden,” is festooned prominently on its products. Though it does not necessarily inspire confidence that the company’s dishwashers are better than the competition, it certainly makes clear Asko’s national origins. Absolut Vodka’s tag—in uncharacteristically unidiomatic English—“Country of Sweden,” does much the same. Saab hawks its cars as “Born from Jets,” an unsubtle allusion to the company’s standing as a pillar of the Swedish military-industrial complex.

scholarly journals Early Discoverers XXXII: P. A. Tutkovskiy and the Glacial Theory of Loess Formation

1978 ◽  
Vol 20 (83) ◽  
pp. 405-408 ◽  
Author(s):  
I. J. Smalley
Keyword(s):  
Close Relationship ◽  
Better Than

Abstract In 1899 P. A. Tutkovskiy published a theory of loess formation which depended on the presence of large continental glaciers. Unfortunately there was no glacial requirement in the theories of Berg and Richthofen and these have survived better than that of Tutkovskiy with the result that the close relationship between glacial action and loess formation is sometimes overlooked.

scholarly journals EMPIRICAL DISTRIBUTIONS OF LAPLACIAN MATRICES OF LARGE DILUTE RANDOM GRAPHS

2012 ◽  
Vol 01 (03) ◽  
pp. 1250004 ◽  
Author(s):  
TIEFENG JIANG
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Spectral Properties ◽  
Empirical Distribution ◽  
Laplacian Matrix ◽  
Free Convolution ◽  
Laplacian Matrices ◽  
Empirical Distributions ◽  
Large N ◽  
Eigenvalues Of The Laplacian

We study the spectral properties of the Laplacian matrices and the normalized Laplacian matrices of the Erdös–Rényi random graph G(n, pn) for large n. Although the graph is simple, we discover some interesting behaviors of the two Laplacian matrices. In fact, under the dilute case, that is, pn ∈ (0, 1) and npn → ∞, we prove that the empirical distribution of the eigenvalues of the Laplacian matrix converges to a deterministic distribution, which is the free convolution of the semi-circle law and N(0, 1). However, for its normalized version, we prove that the empirical distribution converges to the semi-circle law.

scholarly journals Spatial Distribution and Temporal Development of Fusarium Crown and Root Rot of Tomato and Pathogen Dissemination in Field Soil

1999 ◽  
Vol 89 (9) ◽  
pp. 831-839 ◽  
Author(s):  
Yael Rekah ◽  
D. Shtienberg ◽  
J. Katan
Keyword(s):  
Spatial Distribution ◽  
Root Rot ◽  
Logistic Model ◽  
Plant Root ◽  
Growing Season ◽  
Temporal Development ◽  
Simulation Experiments ◽  
Crown And Root Rot ◽  
Plant Simulation ◽  
Better Than

The spatial distribution and temporal development of tomato crown and root rot, caused by Fusarium oxysporum f. sp. radicis-lycopersici, were studied in naturally infested fields in 1996 and 1997. Disease progression fit a logistic model better than a monomolecular one. Geostatistical analyses and semivariogram calculations revealed that the disease spreads from infected plants to a distance of 1.1 to 4.4 m during the growing season. By using a chlorate-resistant nitrate nonutilizing (nit) mutant of F. oxysporum f. sp. radicis-lycopersici as a “tagged” inoculum, the pathogen was found to spread from one plant to the next via infection of the roots. The pathogen spread to up to four plants (2.0 m) on either side of the inoculated focus plant. Root colonization by the nit mutant showed a decreasing gradient from the site of inoculation to both sides of the inoculated plant. Simulation experiments in the greenhouse further established that this soilborne pathogen can spread from root to root during the growing season. These findings suggest a polycyclic nature of F. oxysporum f. sp. radicis-lycopersici, a deviation from the monocyclic nature of many nonzoosporic soilborne pathogens.

scholarly journals A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm

2021 ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Public Dataset ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.

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