Log in

Relevant bibliographies by topics / Clustering coefficient / Journal articles

Journal articles on the topic 'Clustering coefficient'

To see the other types of publications on this topic, follow the link: Clustering coefficient.

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Clustering coefficient.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Bloznelis, Mindaugas, and Valentas Kurauskas. "Clustering function: another view on clustering coefficient." Journal of Complex Networks 4, no. 1 (April 13, 2015): 61–86. http://dx.doi.org/10.1093/comnet/cnv010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

MATSUO, Yutaka. "Clustering Algorithm by Graph Partition using Clustering Coefficient." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 3 (2003): 318–22. http://dx.doi.org/10.3156/jsoft.15.318.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Yu, Pei, Qiang Guo, Ren-De Li, Jing-Ti Han, and Jian-Guo Liu. "Roles of clustering properties for degree-mixing pattern networks." International Journal of Modern Physics C 28, no. 03 (March 2017): 1750029. http://dx.doi.org/10.1142/s0129183117500292.

Full text

Abstract:

The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely [Formula: see text] and [Formula: see text], are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients [Formula: see text] and [Formula: see text] are reported, one can find that the square structures would increase as the degree [Formula: see text] of nodes increasing in disassortative networks. At the same time, the Pearson coefficient [Formula: see text] between the clustering coefficients [Formula: see text] and [Formula: see text] is calculated for networks with different assortative coefficients. The Pearson coefficient [Formula: see text] changes from [Formula: see text] to 0.98 as the assortative coefficient [Formula: see text] increasing from [Formula: see text] to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients [Formula: see text] and [Formula: see text] for networks with tunable assortative coefficients and find that the clustering coefficient [Formula: see text] increases from 0.0038 to 0.5952 while the clustering coefficient [Formula: see text] increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.

APA, Harvard, Vancouver, ISO, and other styles

4

Schank, Thomas, and Dorothea Wagner. "Approximating Clustering Coefficient and Transitivity." Journal of Graph Algorithms and Applications 9, no. 2 (2005): 265–75. http://dx.doi.org/10.7155/jgaa.00108.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ruan, Yuhong, and Anwei Li. "Influence of Dynamical Change of Edges on Clustering Coefficients." Discrete Dynamics in Nature and Society 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/172720.

Full text

Abstract:

Clustering coefficient is a very important measurement in complex networks, and it describes the average ratio between the actual existent edges and probable existent edges in the neighbor of one vertex in a complex network. Besides, in a complex networks, the dynamic change of edges can trigger directly the evolution of network and further affect the clustering coefficients. As a result, in this paper, we investigate the effects of the dynamic change of edge on the clustering coefficients. It is illustrated that the increase and decrease of the clustering coefficient can be effectively controlled by adding or deleting several edges of the network in the evolution of complex networks.

APA, Harvard, Vancouver, ISO, and other styles

6

Cooksey, Ray W., and Geoffrey N. Soutar. "Coefficient Beta and Hierarchical Item Clustering." Organizational Research Methods 9, no. 1 (January 2006): 78–98. http://dx.doi.org/10.1177/1094428105283939.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Wu, Zhihao, Youfang Lin, Jing Wang, and Steve Gregory. "Link prediction with node clustering coefficient." Physica A: Statistical Mechanics and its Applications 452 (June 2016): 1–8. http://dx.doi.org/10.1016/j.physa.2016.01.038.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Gentner, Michael, Irene Heinrich, Simon Jäger, and Dieter Rautenbach. "Large values of the clustering coefficient." Discrete Mathematics 341, no. 1 (January 2018): 119–25. http://dx.doi.org/10.1016/j.disc.2017.08.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

黄, 子轩. "Link Prediction Based on Clustering Coefficient." Applied Physics 04, no. 06 (2014): 101–6. http://dx.doi.org/10.12677/app.2014.46014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Pandove, Divya, Shivani Goel, and Rinkle Rani. "General correlation coefficient based agglomerative clustering." Cluster Computing 22, no. 2 (November 2, 2018): 553–83. http://dx.doi.org/10.1007/s10586-018-2863-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Pedersen, Mangor, Amir Omidvarnia, Jennifer M. Walz, Andrew Zalesky, and Graeme D. Jackson. "Spontaneous brain network activity: Analysis of its temporal complexity." Network Neuroscience 1, no. 2 (June 2017): 100–115. http://dx.doi.org/10.1162/netn_a_00006.

Full text

Abstract:

The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node) and participation coefficients (the between-module connectivity of a node) were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1) Entropy is higher for the participation coefficient than for the clustering coefficient. (2) The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3) The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy.

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Xiao-Lu, Shu-Wei Jia, and Yan Gu. "Empirical analysis of the user reputation and clustering property for user-object bipartite networks." International Journal of Modern Physics C 30, no. 05 (May 2019): 1950035. http://dx.doi.org/10.1142/s0129183119500359.

Full text

Abstract:

User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.

APA, Harvard, Vancouver, ISO, and other styles

13

MCASSEY, MICHAEL P., and FETSJE BIJMA. "A clustering coefficient for complete weighted networks." Network Science 3, no. 2 (January 9, 2015): 183–95. http://dx.doi.org/10.1017/nws.2014.26.

Full text

Abstract:

AbstractThe clustering coefficient is typically used as a measure of the prevalence of node clusters in a network. Various definitions for this measure have been proposed for the cases of networks having weighted edges which may or not be directed. However, these techniques consistently assume that only a subset of all possible edges is present in the network, whereas there are weighted networks of interest in which all possible edges are present, that is, complete weighted networks. For this situation, the concept of clustering is redefined, and computational techniques are presented for computing an associated clustering coefficient for complete weighted undirected or directed networks. The performance of this new definition is compared with that of current clustering definitions when extended to complete weighted networks.

APA, Harvard, Vancouver, ISO, and other styles

14

Goldstein, Rutherford, and Michael S. Vitevitch. "Phonological neighborhood clustering coefficient influences word learning." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 2076. http://dx.doi.org/10.1121/1.4755655.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Santiago, Caio, Vivian Pereira, and Luciano Digiampietri. "Homology Detection Using Multilayer Maximum Clustering Coefficient." Journal of Computational Biology 25, no. 12 (December 2018): 1328–38. http://dx.doi.org/10.1089/cmb.2017.0266.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Lattanzi, Silvio, and Stefano Leonardi. "Efficient computation of the Weighted Clustering Coefficient." Internet Mathematics 12, no. 6 (June 20, 2016): 381–401. http://dx.doi.org/10.1080/15427951.2016.1198281.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Nascimento, Mariá C. V., and André C. P. L. F. Carvalho. "A graph clustering algorithm based on a clustering coefficient for weighted graphs." Journal of the Brazilian Computer Society 17, no. 1 (December 21, 2010): 19–29. http://dx.doi.org/10.1007/s13173-010-0027-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Paembonan, Solmin, and Hisma Abduh. "Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat." PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik 6, no. 2 (September 2, 2021): 48. http://dx.doi.org/10.51557/pt_jiit.v6i2.659.

Full text

Abstract:

Dalam penelitian ini menggunakan metode k-means, metode ini dapat digunakan untuk menjadikan beberapa obat yang mirip menjadi suatu kelompok data tertentu. Salah satu cara untuk mengetahui tingkat kemiripan data adalah melalui perhitungan jarak antar data. Semakain kecil jarak antar data semakin tinggi tingkat kemiripan data tersebut dan sebaliknya semakin besar jarak antar data maka semakin rendah tingkat kemiripannya. Tujuan akhir clustering adalah untuk menentukan kelompok dalam sekumpulan data yang tidak berlabel, karena clustering merupakan suatu metode unsupervised dan tidak terdapat suatu kondisi awal untuk sejumlah cluster yang mungkin terbentuk dalam sekumpulan data, maka dibutuhkan suatu evaluasi hasil clustering. Berdasarkan evaluasi yang dilakukan terhadap hasil clustering dengan nilai dari silhouette coeficient = 0,4854. In this study using the k-means method, this method can be used to make several similar drugs into a certain data group. One way to determine the level of similarity of the data is through the calculation of the distance between the data. The smaller the distance between the data, the higher the level of similarity between the data and vice versa, the greater the distance between the data, the lower the similarity level. For a number of clusters that may be formed in a data set, an evaluation of the results of clustering is needed. Based on the evaluation carried out on the results of clustering with the value of the silhouette coefficient = 0.4854.

APA, Harvard, Vancouver, ISO, and other styles

19

Mogaraju, Jagadish Kumar. "Agglomerative and Divisive hierarchical cluster analysis of groundwater quality variables using opensource tools over YSR district, AP, India." Journal of Scientific Research 66, no. 04 (2022): 15–20. http://dx.doi.org/10.37398/jsr.2022.660403.

Full text

Abstract:

Groundwater quality variables like F, Total Hardness (TH), Total Alkalinity (TA), Total Dissolved Solids (TDS), SO4, SAR, NA, EC, Cl, Ca, Mg, and pH were tested with Hierarchical clustering analysis (HCA) to identify the groupings or clusters that exist in the dataset. The dataset is subjected to Agglomerative and divisive hierarchical clustering. The observations were scaled to compare variables systematically. The clustering structure was determined using an agglomerative coefficient. Agglomerative approaches like complete, average, single, and ward are tested using agglomerative coefficients. The ward approach best suits the dataset to investigate a strong clustering structure. The agglomerative coefficient obtained is 0.8666752, and the divisive coefficient is 0.8371531. The entanglement score attained was 0.26, demonstrating a good alignment with nominal entanglement. The principal component analysis resulted in two main components contributing 54.8% and 18.2% explainable variance. The variables that are prominent in each PC are investigated and reported. The gap statistic and average silhouette method are used to know the optimal number of clusters. Open-source software like R/ R studio is used for this analysis. This work concludes that clustering analysis is essential to understand the groundwater quality variables better.

APA, Harvard, Vancouver, ISO, and other styles

20

SARAÇLI, Sinan, and Murat AKŞİT. "Büyük Veride Hiyerarşik Kümeleme Yöntemlerinin Kofenetik Korelasyon Katsayısı ile Karşılaştırılması." Afyon Kocatepe University Journal of Sciences and Engineering 22, no. 3 (June 30, 2022): 552–59. http://dx.doi.org/10.35414/akufemubid.1018302.

Full text

Abstract:

The aim of this study is to compare hierarchical clustering methods by Cophenetic Correlation Coefficient (CCC) when there is a big data. For this purpose, after giving information about big data, clustering methods and CCC, analyzes are carried out for the related data set. The 2015 air travel consumer report, which was used in the application part of the study and published by the US Ministry of Transport, was used as big data. Libraries of the Python programming language installed on the Amazon cloud server, which includes open-source big data technologies, were used for data analysis. Since there is big data in the study, in order to save time and economy, the variables used in the study were first reduced by feature selection method, standardized and analyzed over the final 4 different data sets. As a result of the clustering analysis, it was observed that the highest CCC was obtained with the Average clustering method for all of these four different data sets.

APA, Harvard, Vancouver, ISO, and other styles

21

Rahman, Zahid, Altaf Hussain, Hussain Shah, and Muhammad Arshad. "Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (February 8, 2022): 381–99. http://dx.doi.org/10.14201/adcaij2021104381399.

Full text

Abstract:

Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Zhen Bo, and Bao Zhi Qiu. "Fuzzy C-Means Clustering Algorithm Based on Coefficient of Variation." Advanced Materials Research 998-999 (July 2014): 873–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.873.

Full text

Abstract:

To reduce the impact of irrelevant attributes on clustering results, and improve the importance of relevant attributes to clustering, this paper proposes fuzzy C-means clustering algorithm based on coefficient of variation (CV-FCM). In the algorithm, coefficient of variation is used to weigh attributes so as to assign different weights to each attribute in the data set, and the magnitude of weight is used to express the importance of different attributes to clusters. In addition, for the characteristic of fuzzy C-means clustering algorithm that it is susceptible to initial cluster center value, the method for the selection of initial cluster center based on maximum distance is introduced on the basis of weighted coefficient of variation. The result of the experiment based on real data sets shows that this algorithm can select cluster center effectively, with the clustering result superior to general fuzzy C-means clustering algorithms.

APA, Harvard, Vancouver, ISO, and other styles

23

Kooij, Robert E., Nikolaj Horsevad Sørensen, and Roland Bouffanais. "Tuning the clustering coefficient of generalized circulant networks." Physica A: Statistical Mechanics and its Applications 578 (September 2021): 126088. http://dx.doi.org/10.1016/j.physa.2021.126088.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Morita, Satoru. "Evolutionary game on networks with high clustering coefficient." Nonlinear Theory and Its Applications, IEICE 7, no. 2 (2016): 110–17. http://dx.doi.org/10.1587/nolta.7.110.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

LI, CHONG, SHI-ZE GUO, ZHE-MING LU, YU-LONG QIAO, and GUANG-HUA SONG. "A NEW CENTRALITY METRIC BASED ON CLUSTERING COEFFICIENT." International Journal of Modern Physics C 24, no. 07 (June 6, 2013): 1350043. http://dx.doi.org/10.1142/s0129183113500435.

Full text

Abstract:

Many centrality metrics have been proposed over the years to compute the centrality of nodes, which has been a key issue in complex network analysis. The most important node can be estimated through a variety of metrics, such as degree, closeness, eigenvector, betweenness, flow betweenness, cumulated nominations and subgraph. Simulated flow is a common method adopted by many centrality metrics, such as flow betweenness centrality, which assumes that the information spreads freely in the entire network. Generally speaking, the farther the information travels, the more times the information passes the geometric center. Thus, it is easy to determine which node is more likely to be the center of the geometry network. However, during information transmission, different nodes do not share the same vitality, and some nodes are more active than others. Therefore, the product of one node's degree and its clustering coefficient can be viewed as a good factor to show how active this node is. In this paper, a new centrality metric called vitality centrality is introduced, which is only based on this product and the simulated flow. Simulation experiments based on six test networks have been carried out to demonstrate the effectiveness of our new metric.

APA, Harvard, Vancouver, ISO, and other styles

26

Iskhakov, L. N., M. S. Mironov, L. A. Prokhorenkova, B. Kamiński, and P. Prałat. "Clustering Coefficient of a Spatial Preferential Attachment Model." Doklady Mathematics 98, no. 1 (July 2018): 304–7. http://dx.doi.org/10.1134/s1064562418050046.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Teraji, Tetsuro, and Norikazu Takahashi. "On Graphs that Locally Maximize Global Clustering Coefficient." IEICE Proceeding Series 2 (March 17, 2014): 130–33. http://dx.doi.org/10.15248/proc.2.130.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Han, Jingti, and Changmei Mao. "Roles of Clustering Coefficient for the Network Reconstruction." Mathematical Problems in Engineering 2018 (October 24, 2018): 1–11. http://dx.doi.org/10.1155/2018/4949673.

Full text

Abstract:

It is important to establish relations between the network reconstruction and the topological dynamical structure of networks. In this article, we quantify the effect for two types of network topologies on the performance of network reconstruction. First, we generate two network modes with variable clustering coefficient based on Holme-Kim model and Newman-Watts small-world model, then we reconstruct the artificial networks by using a novel framework called L1-norm minimization algorithm based on a theory called compressive sensing (CS), a framework for recovering sparse signals. The results of the simulation experiment show that the accuracy rate for the network reconstruction is a monotonically increasing function of the clustering coefficient in Holme-Kim model, whereas the opposite occurs in Newman-Watts small-world network. And this yet demonstrates that the larger the network size, the higher the accuracy rate. Morever, we compare the results of CS with orthogonal matching pursuit (OMP), a greedy algorithm. The results show that the accuracy rate of L1-norm minimization method is 10% higher than that of OMP, and OMP yields 1.2 times the computation speed of L1-norm minimization. Our work demonstrates that the topological structure of network has influence on the accurate reconstruction and it is helpful for offering proper method for the network reconstruction.

APA, Harvard, Vancouver, ISO, and other styles

29

Rodríguez-Méndez, Victor, Enrico Ser-Giacomi, and Emilio Hernández-García. "Clustering coefficient and periodic orbits in flow networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 27, no. 3 (March 2017): 035803. http://dx.doi.org/10.1063/1.4971787.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Zhang, Peng, Jinliang Wang, Xiaojia Li, Menghui Li, Zengru Di, and Ying Fan. "Clustering coefficient and community structure of bipartite networks." Physica A: Statistical Mechanics and its Applications 387, no. 27 (December 2008): 6869–75. http://dx.doi.org/10.1016/j.physa.2008.09.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Li, Xuefei, Lijun Chang, Kai Zheng, Zi Huang, and Xiaofang Zhou. "Ranking weighted clustering coefficient in large dynamic graphs." World Wide Web 20, no. 5 (October 11, 2016): 855–83. http://dx.doi.org/10.1007/s11280-016-0420-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Gerhardt, Günther J. L., Ney Lemke, and Gilberto Corso. "Network clustering coefficient approach to DNA sequence analysis." Chaos, Solitons & Fractals 28, no. 4 (May 2006): 1037–45. http://dx.doi.org/10.1016/j.chaos.2005.08.138.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Sai, L. Nitya, M. Sai Shreya, A. Anjan Subudhi, B. Jaya Lakshmi, and K. B. Madhuri. "Optimal K-Means Clustering Method Using Silhouette Coefficient." International Journal of Applied Research on Information Technology and Computing 8, no. 3 (2017): 335. http://dx.doi.org/10.5958/0975-8089.2017.00030.6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Liu, Xue, Xiaoping Zeng, Zhiming Wang, Bin Zhu, and Li Chen. "The Clustering Coefficient of Multiple Parallel Airlines AANET." International Journal of Future Generation Communication and Networking 9, no. 7 (July 31, 2016): 135–44. http://dx.doi.org/10.14257/ijfgcn.2016.9.7.13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Ostroumova Prokhorenkova, Liudmila. "General results on preferential attachment and clustering coefficient." Optimization Letters 11, no. 2 (April 6, 2016): 279–98. http://dx.doi.org/10.1007/s11590-016-1030-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Fukami, Tatsuya, and Norikazu Takahashi. "New classes of clustering coefficient locally maximizing graphs." Discrete Applied Mathematics 162 (January 2014): 202–13. http://dx.doi.org/10.1016/j.dam.2013.09.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Kumar, Ajay, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas. "Level-2 node clustering coefficient-based link prediction." Applied Intelligence 49, no. 7 (February 4, 2019): 2762–79. http://dx.doi.org/10.1007/s10489-019-01413-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Altieri, Nicholas, Thomas Gruenenfelder, and David B. Pisoni. "Clustering coefficients of lexical neighborhoods." Mental Lexicon 5, no. 1 (June 18, 2010): 1–21. http://dx.doi.org/10.1075/ml.5.1.01alt.

Full text

Abstract:

High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) — a graph theoretic variable measuring the degree to which a word’s neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.

APA, Harvard, Vancouver, ISO, and other styles

39

GUO, QIANG, RUI LENG, KERUI SHI, and JIAN-GUO LIU. "INFORMATION FILTERING VIA CLUSTERING COEFFICIENTS OF USER–OBJECT BIPARTITE NETWORKS." International Journal of Modern Physics C 23, no. 02 (February 2012): 1250012. http://dx.doi.org/10.1142/s012918311250012x.

Full text

Abstract:

The clustering coefficient of user–object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user–object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user–object bipartite networks should be investigated to estimate users' tastes.

APA, Harvard, Vancouver, ISO, and other styles

40

Zhang, Juping, Chan Yang, Zhen Jin, and Jia Li. "Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks." Journal of Theoretical Biology 449 (July 2018): 1–13. http://dx.doi.org/10.1016/j.jtbi.2018.04.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Oliveira, R. I., R. Ribeiro, and R. Sanchis. "Disparity of clustering coefficients in the Holme‒Kim network model." Advances in Applied Probability 50, no. 3 (September 2018): 918–43. http://dx.doi.org/10.1017/apr.2018.41.

Full text

Abstract:

Abstract The Holme‒Kim random graph process is a variant of the Barabási‒Álbert scale-free graph that was designed to exhibit clustering. In this paper we show that whether the model does indeed exhibit clustering depends on how we define the clustering coefficient. In fact, we find that the local clustering coefficient typically remains positive whereas global clustering tends to 0 at a slow rate. These and other results are proven via martingale techniques, such as Freedman's concentration inequality combined with a bootstrapping argument.

APA, Harvard, Vancouver, ISO, and other styles

42

Liu, Yongxin, Bin Song, Linong Wang, Jiachen Gao, and Rihong Xu. "Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN." Applied Sciences 10, no. 13 (June 27, 2020): 4440. http://dx.doi.org/10.3390/app10134440.

Full text

Abstract:

The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil and enlarged the correlation of the same fault category by introducing the amplification coefficient, and finally we used the density clustering method to cluster diagnosis. The experimental results show that the accuracy of clustering is improved by 32.7% compared with the direct clustering judgment without using correlation coefficient, which can effectively cluster different types of transformers fault modes. This method provides a new idea for transformers fault identification, and has practical application value.

APA, Harvard, Vancouver, ISO, and other styles

43

Akhtar, Adil, and Tazid Ali. "Analysis of Unweighted Amino Acids Network." International Scholarly Research Notices 2014 (December 16, 2014): 1–6. http://dx.doi.org/10.1155/2014/350276.

Full text

Abstract:

The analysis of amino acids network is very important to studying the various physicochemical properties of amino acids. In this paper we consider the amino acid network based on mutation of the codons. To analyze the relative importance of the amino acids we have discussed different measures of centrality. The measure of centrality is a powerful tool of graph theory for ranking the vertices and analysis of biological network. We have also investigated the correlation coefficients between various measures of centrality. Also we have discussed clustering coefficient as well as average clustering coefficient of the network. Finally we have discussed the degree of distribution as well as skewness.

APA, Harvard, Vancouver, ISO, and other styles

44

Silva, Anderson Rodrigo da, and Carlos Tadeu dos Santos Dias. "A cophenetic correlation coefficient for Tocher's method." Pesquisa Agropecuária Brasileira 48, no. 6 (June 2013): 589–96. http://dx.doi.org/10.1590/s0100-204x2013000600003.

Full text

Abstract:

The objective of this work was to propose a way of using the Tocher's method of clustering to obtain a matrix similar to the cophenetic one obtained for hierarchical methods, which would allow the calculation of a cophenetic correlation. To illustrate the obtention of the proposed cophenetic matrix, we used two dissimilarity matrices - one obtained with the generalized squared Mahalanobis distance and the other with the Euclidean distance - between 17 garlic cultivars, based on six morphological characters. Basically, the proposal for obtaining the cophenetic matrix was to use the average distances within and between clusters, after performing the clustering. A function in R language was proposed to compute the cophenetic matrix for Tocher's method. The empirical distribution of this correlation coefficient was briefly studied. For both dissimilarity measures, the values of cophenetic correlation obtained for the Tocher's method were higher than those obtained with the hierarchical methods (Ward's algorithm and average linkage - UPGMA). Comparisons between the clustering made with the agglomerative hierarchical methods and with the Tocher's method can be performed using a criterion in common: the correlation between matrices of original and cophenetic distances.

APA, Harvard, Vancouver, ISO, and other styles

45

Liu, Xin, Jiang Wu, Chen Yang, and Wenjun Jiang. "A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection." Risks 6, no. 4 (October 9, 2018): 115. http://dx.doi.org/10.3390/risks6040115.

Full text

Abstract:

In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

APA, Harvard, Vancouver, ISO, and other styles

46

Khang, Tran Dinh, Nguyen Duc Vuong, Manh-Kien Tran, and Michael Fowler. "Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients." Algorithms 13, no. 7 (June 30, 2020): 158. http://dx.doi.org/10.3390/a13070158.

Full text

Abstract:

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

APA, Harvard, Vancouver, ISO, and other styles

47

Ren, Xiaogang, Yue Wu, and Zhiying Cao. "Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI." Journal of Healthcare Engineering 2021 (September 25, 2021): 1–10. http://dx.doi.org/10.1155/2021/3937222.

Full text

Abstract:

Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.

APA, Harvard, Vancouver, ISO, and other styles

48

Górecki, J., M. Hofert, and M. Holeňa. "Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas." Dependence Modeling 5, no. 1 (January 26, 2017): 75–87. http://dx.doi.org/10.1515/demo-2017-0005.

Full text

Abstract:

Abstract Several successful approaches to structure determination of hierarchical Archimedean copulas (HACs) proposed in the literature rely on agglomerative clustering and Kendall’s correlation coefficient. However, there has not been presented any theoretical proof justifying such approaches. This work fills this gap and introduces a theorem showing that, given the matrix of the pairwise Kendall correlation coefficients corresponding to a HAC, its structure can be recovered by an agglomerative clustering technique.

APA, Harvard, Vancouver, ISO, and other styles

49

Luo, Dang, Manman Zhang, and Huihui Zhang. "Two-stage grey cloud clustering model for drought risk assessment." Grey Systems: Theory and Application 10, no. 1 (November 15, 2019): 68–84. http://dx.doi.org/10.1108/gs-06-2019-0021.

Full text

Abstract:

Purpose The purpose of this paper is to establish a two-stage grey cloud clustering model to assess the drought risk level of 18 prefecture-level cities in Henan Province. Design/methodology/approach The clustering process is divided into two stages. In the first stage, grey cloud clustering coefficient vectors are obtained by grey cloud clustering. In the second stage, with the help of the weight kernel clustering function, the general representation of the weight vector group of kernel clustering is given. And a new coefficient vector of kernel clustering that integrates the support factors of the adjacent components was obtained in this stage. The entropy resolution coefficient of grey cloud clustering coefficient vector is set as the demarcation line of the two stages, and a two-stage grey cloud clustering model, which combines grey and randomness, is proposed. Findings This paper demonstrates that 18 cities in Henan Province are divided into five categories, which are in accordance with five drought hazard levels. And the rationality and validity of this model is illustrated by comparing with other methods. Practical implications This paper provides a practical and effective new method for drought risk assessment and, then, provides theoretical support for the government and production departments to master drought information and formulate disaster prevention and mitigation measures. Originality/value The model in this paper not only solves the problem that the result and the rule of individual subjective judgment are always inconsistent owing to not fully considering the randomness of the possibility function, but also solves the problem that it’s difficult to ascertain the attribution of decision objects, when several components of grey clustering coefficient vector tend to be balanced. It provides a new idea for the development of the grey clustering model. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

APA, Harvard, Vancouver, ISO, and other styles

50

LIU, JIANGUO, LEI HOU, YI-LU ZHANG, WEN-JUN SONG, and XUE PAN. "EMPIRICAL ANALYSIS OF THE CLUSTERING COEFFICIENT IN THE USER-OBJECT BIPARTITE NETWORKS." International Journal of Modern Physics C 24, no. 08 (July 3, 2013): 1350055. http://dx.doi.org/10.1142/s0129183113500551.

Full text

Abstract:

The clustering coefficient of the bipartite network, C4, has been widely used to investigate the statistical properties of the user-object systems. In this paper, we empirically analyze the evolution patterns of C4 for a nine year MovieLens data set, where C4 is used to describe the diversity of the user interest. First, we divide the MovieLens data set into fractions according to the time intervals and calculate C4 of each fraction. The empirical results show that, the diversity of the user interest changes periodically with a round of one year, which reaches the smallest value in spring, then increases to the maximum value in autumn and begins to decrease in winter. Furthermore, a null model is proposed to compare with the empirical results, which is constructed in the following way. Each user selects each object with a turnable probability p, and the numbers of users and objects are equal to that of the real MovieLens data set. The comparison result indicates that the user activity has greatly influenced the structure of the user-object bipartite network, and users with the same degree information may have two totally different clustering coefficients. On the other hand, the same clustering coefficient also corresponds to different degrees. Therefore, we need to take the clustering coefficient into consideration together with the degree information when describing the user selection activity.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!