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Artykuły w czasopismach na temat „Two party clustering”

Autor: Grafiati

Data publikacji: 6 września 2023

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1

Kumari, Priya, and Seema Maitrey. "Two Party Hierarichal Clustering Over Horizontally Partitioned Data Set." International Journal of Data Mining & Knowledge Management Process 7, no. 3 (2017): 33–43. http://dx.doi.org/10.5121/ijdkp.2017.7303.

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Tripathy, Animesh, and Ipsa De. "Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset." Journal of Software Engineering and Applications 06, no. 05 (2013): 26–31. http://dx.doi.org/10.4236/jsea.2013.65b006.

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Bunn, Paul, and Rafail Ostrovsky. "Oblivious Sampling with Applications to Two-Party k-Means Clustering." Journal of Cryptology 33, no. 3 (2020): 1362–403. http://dx.doi.org/10.1007/s00145-020-09349-w.

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Feldman, Nizan, and Tal Sadeh. "War and Third-party Trade." Journal of Conflict Resolution 62, no. 1 (2016): 119–42. http://dx.doi.org/10.1177/0022002716644329.

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Few studies explain how wars affect trade with third parties. We argue that wartime trade policies should raise trade with friendly and enemy-hostile third parties but reduce trade with hostile and enemy-friendly third parties. At the same time, the private motivation of firms and households may be incompatible with national wartime trade policies and constrain the effectiveness of wartime trade policies. Our directed dyadic data set consists of almost all of the states from 1885 to 2000. Running a high definition fixed effects regression with two-way clustering of standard errors, we find tha

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Jiang, Zoe L., Ning Guo, Yabin Jin, et al. "Efficient two-party privacy-preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing." Information Sciences 518 (May 2020): 168–80. http://dx.doi.org/10.1016/j.ins.2019.12.051.

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Lee, Hyun-Chool, and Alexandre Repkine. "Do Political Parties Represent Voters’ Preference? A Clustering Analysis of Korea’s Local Election of 2018." Philippine Political Science Journal 44, no. 2 (2023): 121–51. http://dx.doi.org/10.1163/2165025x-bja10045.

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Abstract We employ the results of a survey conducted on two thousand voters that have participated in Korean local election of 2018 to perform clustering analysis of Korean voters’ preferences. We attempt to test the hypothesis of these preferences being adequately represented by the five major political parties. We find that there was likely a significant mismatch between Korean voters and the five voting camps identified under the assumption of five being an optimal number of the voting clusters. After relaxing this assumption we found that the optimal number of voting camps in Korea is two

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Ping, Yuan, Bin Hao, Xiali Hei, Jie Wu, and Baocang Wang. "Maximized Privacy-Preserving Outsourcing on Support Vector Clustering." Electronics 9, no. 1 (2020): 178. http://dx.doi.org/10.3390/electronics9010178.

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Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a

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A, Pranav Shriram, Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, and Bhavish Raj Gopal. "Find Thy Neighbourhood: Privacy-Preserving Local Clustering." Proceedings on Privacy Enhancing Technologies 2023, no. 2 (2023): 23–39. http://dx.doi.org/10.56553/popets-2023-0039.

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Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MP

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Oswald, Michael T., Meike Fromm, and Elena Broda. "Strategic clustering in right-wing-populism? ‘Green policies’ in Germany and France." Zeitschrift für Vergleichende Politikwissenschaft 15, no. 2 (2021): 185–205. http://dx.doi.org/10.1007/s12286-021-00485-6.

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AbstractPast research pointed to the idea that right-wing ideology and climate-change skepticism are inherently linked. Empirical reality proves differently however, since right-wing populist parties are starting to adapt pro environmentalist stances. In this paper, we look into two prominent cases of diametrical diverging environmental strategies by right-wing-populist-parties: France’s Rassemblement National and Germany’s Alternative für Deutschland. In order convey this point, we use comparative qualitative content analysis and examine several decisive determinants regarding environmental s

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Anglou, Fiorentia Zoi, Stavros Ponis, and Athanasios Spanos. "A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry." Journal of Industrial Engineering and Management 14, no. 3 (2021): 604. http://dx.doi.org/10.3926/jiem.3446.

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Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions.Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by

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Sarddar, Debabrata, Raktim Kumar Dey, Rajesh Bose, and Sandip Roy. "Topic Modeling as a Tool to Gauge Political Sentiments from Twitter Feeds." International Journal of Natural Computing Research 9, no. 2 (2020): 14–35. http://dx.doi.org/10.4018/ijncr.2020040102.

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As ubiquitous as it is, the Internet has spawned a slew of products that have forever changed the way one thinks of society and politics. This article proposes a model to predict chances of a political party winning based on data collected from Twitter microblogging website, because it is the most popular microblogging platform in the world. Using unsupervised topic modeling and the NRC Emotion Lexicon, the authors demonstrate how it is possible to predict results by analyzing eight types of emotions expressed by users on Twitter. To prove the results based on empirical analysis, the authors e

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Chen, Yi, Hong Wen, Jinsong Wu, et al. "Clustering Based Physical-Layer Authentication in Edge Computing Systems with Asymmetric Resources." Sensors 19, no. 8 (2019): 1926. http://dx.doi.org/10.3390/s19081926.

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In this paper, we propose a clustering based physical-layer authentication scheme (CPAS) to overcome the drawback of traditional cipher-based authentication schemes that suffer from heavy costs and are limited by energy-constrained intelligent devices. CPAS is a novel cross-layer secure authentication approach for edge computing system with asymmetric resources. The CPAS scheme combines clustering and lightweight symmetric cipher with physical-layer channel state information to provide two-way authentication between terminals and edge devices. By taking advantage of temporal and spatial unique

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Kavetskyy, Igor. "Polish electoral space aft er 2001 against the background of rivalry between the two main actors of the political scene." Bulletin of Geography. Socio-economic Series, no. 60 (June 5, 2023): 47–59. http://dx.doi.org/10.12775/bgss-2023-0015.

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Th e aim of the paper is to specify the main patterns – and identify the basic determinants – of the formation of the Polish electoral space aft er 2001, with particular emphasis on factors of a geographical nature, against the background of the rivalry between the two main actors of the political scene, i.e., Civic Platform (Platforma Obywatelska, PO) and Law and Justice (Prawo i Sprawiedliwość, PiS). Using spatial econometric tools, it was found that the PO–PiS oligopoly on the Polish political party market results in increasing spatial clustering and geographical polarisation of voters of b

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Cavallaro, Matteo, David Flacher, and Massimo Angelo Zanetti. "Radical right parties and European economic integration: Evidence from the seventh European Parliament." European Union Politics 19, no. 2 (2018): 321–43. http://dx.doi.org/10.1177/1465116518760241.

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This article explores the differences in radical right parties' voting behaviour on economic matters at the European Parliament. As the literature highlights the heterogeneity of these parties in relation to their economic programmes, we test whether divergences survive the elections and translate into dissimilar voting patterns. Using voting records from the seventh term of the European Parliament, we show that radical right parties do not act as a consolidated party family. We then analyse the differences between radical right parties by the means of different statistical methods (NOMINATE,

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Lofgren, Hans. "Generic drugs: international trends and policy developments in Australia." Australian Health Review 27, no. 1 (2004): 39. http://dx.doi.org/10.1071/ah042710039.

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Public and private third-party payers in many countries encourage or mandate the use of generic drugs. This articleexamines the development of generics policy in Australia, against the background of a description of internationaltrends in this area, and related experiences of reference pricing programs. The Australian generics market remainsunderdeveloped due to a historical legacy of small Pharmaceutical Benefits Scheme price differentials betweenoriginator brands and generics. It is argued that policy measures open to the Australian government can be conceivedas clustering around two differe

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khan, Nida Saddaf, and Muhammad Sayeed Ghani. "Predicting Collective Synchronous State of Sentiments for Users in Social Media." July 2019 38, no. 3 (2019): 687–704. http://dx.doi.org/10.22581/muet1982.1903.13.

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The increasing use of social media offers researchers with an opportunity to apply the sentiment analysis techniques over the data collected from social media websites. These techniques promise to provide an insight into the users’ perspectives on many areas. In this research, a sentiment analysis model is proposed based on HMC (Hidden Markov Chains) and K-Means algorithm to predict the collective synchronous state of sentiments for users on social media. HMC are used to find the converged state while K-Means is used to find the representative group of users. For this purpose, we have used dat

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Ramsza, Michał. "Market Choices Driven by Reference Groups: A Comparison of Analytical and Simulation Results on Random Networks." Entropy 23, no. 8 (2021): 1007. http://dx.doi.org/10.3390/e23081007.

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The present paper reports simulation results for a simple model of reference group influence on market choices, e.g., brand selection. The model was simulated on three types of random graphs, Erdos–Renyi, Barabasi–Albert, and Watts–Strogatz. The estimates of equilibria based on the simulation results were compared to the equilibria of the theoretical model. It was verified that the simulations exhibited the same qualitative behavior as the theoretical model, and for graphs with high connectivity and low clustering, the quantitative predictions offered a viable approximation. These results allo

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Jakulin, Aleks, Wray Buntine, Timothy M. La Pira, and Holly Brasher. "Analyzing the U.S. Senate in 2003: Similarities, Clusters, and Blocs." Political Analysis 17, no. 3 (2009): 291–310. http://dx.doi.org/10.1093/pan/mpp006.

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In this paper, we apply information theoretic measures to voting in the U.S. Senate in 2003. We assess the associations between pairs of senators and groups of senators based on the votes they cast. For pairs, we use similarity-based methods, including hierarchical clustering and multidimensional scaling. To identify groups of senators, we use principal component analysis. We also apply a discrete multinomial latent variable model that we have developed. In doing so, we identify blocs of cohesive voters within the Senate and contrast it with continuous ideal point methods. We find more nuanced

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Chiriță, Andrei, and Camelia Delcea. "A Laboratory Experiment for Analyzing Electors’ Strategic Behavior in a First-Past-the-Post System." Symmetry 12, no. 7 (2020): 1081. http://dx.doi.org/10.3390/sym12071081.

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As it is well acknowledged that the electoral system is one of the fundamental rocks of our modern society, the behavior of electors engaged in a voting system is of the utmost importance. In this context, the goal of the study is to model the behavior of voters in a first-past-the-post system and to analyze its consequences on a party system. Among the assumptions of this study is Duverger’s law, which states that first-past-the-post systems favor a two-party system as the voters engage in tactical voting, choosing to vote in favor of a less preferred candidate who has better odds of winning.

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Ullah, Fasee, Izhar Ullah, Atif Khan, M. Irfan Uddin, Hashem Alyami, and Wael Alosaimi. "Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network." Journal of Healthcare Engineering 2020 (November 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/8824907.

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There is a need to develop an effective data preservation scheme with minimal information loss when the patient’s data are shared in public interest for different research activities. Prior studies have devised different approaches for data preservation in healthcare domains; however, there is still room for improvement in the design of an elegant data preservation approach. With that motivation behind, this study has proposed a medical healthcare-IoTs-based infrastructure with restricted access. The infrastructure comprises two algorithms. The first algorithm protects the sensitivity informat

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Nanni, Federico, Goran Glavaš, Ines Rehbein, Simone Paolo Ponzetto, and Heiner Stuckenschmidt. "Political Text Scaling Meets Computational Semantics." ACM/IMS Transactions on Data Science 2, no. 4 (2021): 1–27. http://dx.doi.org/10.1145/3485666.

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During the past 15 years, automatic text scaling has become one of the key tools of the Text as Data community in political science. Prominent text-scaling algorithms, however, rely on the assumption that latent positions can be captured just by leveraging the information about word frequencies in documents under study. We challenge this traditional view and present a new, semantically aware text-scaling algorithm, SemScale , which combines recent developments in the area of computational linguistics with unsupervised graph-based clustering. We conduct an extensive quantitative analysis over a

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Cyperski, Szymon, Paweł D. Domański, and Michał Okulewicz. "Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts." Algorithms 16, no. 8 (2023): 360. http://dx.doi.org/10.3390/a16080360.

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Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A company can have its own trucks, or it can use third-party companies. This transportation can be carried out in a variety of formulas, with full truckload being the most common for long routes. The shipper must be aware of the potential cost of such a service during the process of selecting a particular transport. The presente

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Zhang, Kaiwen, Guyan Dai, and Tianhui Chen. "A study on the impact mechanism of common prosperity development based on the perspective of digital inclusive finance." Highlights in Business, Economics and Management 12 (May 16, 2023): 67–76. http://dx.doi.org/10.54097/hbem.v12i.8316.

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It is an important mission of the Party Central Committee to gradually achieve common prosperity, and it is of great contemporary significance to investigate the mechanism of the impact of digital inclusive finance on common prosperity in depth. Based on the panel data of 31 provinces from 2011-2021, this paper uses the Entropy-Topsis method to measure and quantify the development level of common prosperity and conducts convergence and clustering analysis, and then uses the two-factor fixed effects model to explore the mechanism and heterogeneity of different factors on common prosperity. The

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Sampurno, Global Ilham, Annisa Annisa, and Sony Hartono Wijaya. "Sistem Rekomendasi Dua Arah untuk Pemilihan Dosen Pembimbing Menggunakan Data Histori dan Skyline View Queries." Jurnal Teknologi Informasi dan Ilmu Komputer 9, no. 5 (2022): 1055. http://dx.doi.org/10.25126/jtiik.2022955458.

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<p>Pemilihan dosen pembimbing merupakan salah satu faktor yang mempengaruhi proses penyelesaian tugas akhir. Pada mekanisme pemilihan dosen pembimbing, sering kali mahasiswa sendiri belum memahami dengan jelas kemampuan dirinya serta topik apa yang akan dipilihnya, sehingga nama calon dosen pembimbing yang diusulkan mahasiswa umumnya belum mempertimbangkan hal tersebut. Mekanisme seperti ini juga menyebabkan terjadinya penumpukan calon bimbingan pada dosen tertentu dan kekurangan bimbingan pada dosen yang lain, meskipun keduanya memiliki latar belakang keilmuan yang mirip. Pada saat yang

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Guillard, Robin, Adam Hessas, Louis Korczowski, Alain Londero, Marco Congedo, and Vincent Loche. "Comparing Clustering Methods Applied to Tinnitus within a Bootstrapped and Diagnostic-Driven Semi-Supervised Framework." Brain Sciences 13, no. 4 (2023): 572. http://dx.doi.org/10.3390/brainsci13040572.

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The understanding of tinnitus has always been elusive and is largely prevented by its intrinsic heterogeneity. To address this issue, scientific research has aimed at defining stable and easily identifiable subphenotypes of tinnitus. This would allow better disentangling the multiple underlying pathophysiological mechanisms of tinnitus. In this study, three-dimensionality reduction techniques and two clustering methods were benchmarked on a database of 2772 tinnitus patients in order to obtain a reliable segmentation of subphenotypes. In this database, tinnitus patients’ endotypes (i.e., parts

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Kong, Taewoon, Kyungje Seong, Kiburm Song, and Kichun Lee. "Two-mode modularity clustering of parts and activities for cell formation problems." Computers & Operations Research 100 (December 2018): 77–88. http://dx.doi.org/10.1016/j.cor.2018.06.018.

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Káli, Szabolcs. "Studying the effects of synaptic clustering in silico : when the neighbourhood party gets too loud." Journal of Physiology 596, no. 17 (2018): 3829–30. http://dx.doi.org/10.1113/jp276627.

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Liu, Yongli, Yajun Zhang, and Hao Chao. "Incremental Fuzzy Clustering Based on Feature Reduction." Journal of Electrical and Computer Engineering 2022 (March 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/8566253.

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In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each ch

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Roshanbakht, Nafiseh, and Mohammad Reza Shojaei. "Two-Center Gaussian Potential Well for Studying Light Nucleus in Cluster Structure." Advances in High Energy Physics 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/9309636.

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The clustering phenomena are very important to determine structure of light nuclei and deformation of spherical shape is inevitable. Hence, we calculated the energy levels of two-center Gaussian potential well including spin-orbit coupling by solving the Schrödinger equation in the cylindrical coordinates. This model can predict the spin and parity of the light nuclei that have two identical cluster structures.

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Ding, Lin, Weihong Xu, and Yuantao Chen. "Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group." Complexity 2020 (October 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/8864239.

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Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. This paper proposes improved density peaks clustering based on natural neighbo

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Al-Nuaimi, Dhamyaa H., Muhammad F. Akbar, Laith B. Salman, Intan S. Zainal Abidin, and Nor Ashidi Mat Isa. "AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks." Electronics 10, no. 1 (2021): 76. http://dx.doi.org/10.3390/electronics10010076.

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer

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Zhu, Yingyu, and Simon Li. "Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/2841325.

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The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a two-mode similarity coefficient is applied to simultaneously form machine groups an

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Ni, James, and Terry Wallace. "Temporal Clustering of Earthquakes: Examples from the Basin and Range Province." Seismological Research Letters 59, no. 4 (1988): 316. http://dx.doi.org/10.1785/gssrl.59.4.316.

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Abstract Accurate forecasting of earthquake hazards depends on whether seismicity is time-stationary or variable. Detailed studies of late Quaternary faulting in two parts of the Basin and Range suggest that seismicity cannot be considered time-stationary, and there may be temporal clustering of earthquakes. In west central Nevada there appear to be three “peaks” of activity, (1) the historical activity along the Nevada Seismic Belt, (2) a temporal cluster at 2,000–3,000 ya, (3) a duster at 5,000–6,000 ya. For the two older episodes the ages of faulting are determined by morphologic analysis o

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Liu, Qidong, Ruisheng Zhang, Zhili Zhao, Zhenghai Wang, Mengyao Jiao, and Guangjing Wang. "Robust MST-Based Clustering Algorithm." Neural Computation 30, no. 6 (2018): 1624–46. http://dx.doi.org/10.1162/neco_a_01081.

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Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree

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Huo, Jiaofei, and Xiaomo Yu. "Three-dimensional mechanical parts reconstruction technology based on two-dimensional image." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142091000. http://dx.doi.org/10.1177/1729881420910008.

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With the development of computer technology and three-dimensional reconstruction technology, three-dimensional reconstruction based on visual images has become one of the research hotspots in computer graphics. Three-dimensional reconstruction based on visual image can be divided into three-dimensional reconstruction based on single photo and video. As an indirect three-dimensional modeling technology, this method is widely used in the fields of film and television production, cultural relics restoration, mechanical manufacturing, and medical health. This article studies and designs a stereo v

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Zhao, Qingchao, Long Li, Yan Chu, Zhen Yang, Zhengkui Wang, and Wen Shan. "Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks." Remote Sensing 14, no. 15 (2022): 3768. http://dx.doi.org/10.3390/rs14153768.

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In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms are usually time-consuming and limit their applications. In order to infer the connectivity between image instances, they usually created a subgraph for each image instance. Due to the creation and process of a large number of subgraphs as the input of GNN, the computation overheads are enormous. To address the high computation overhead problem in t

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Sapozhnikov, Sergei, and Dana Kovaleva. "Application of clustering algorithm to wide stellar pairs for unsupervised search of parts of disrupting clusters." Open Astronomy 30, no. 1 (2021): 191–202. http://dx.doi.org/10.1515/astro-2021-0025.

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Abstract We introduce the application of the clustering algorithm to the preliminary compiled list of probable wide pairs of co-moving stars. The main aim of such development is a possibility of unsupervised blind search of coeval loose stellar structures over significant regions in space. Using Gaia EDR3 data, we investigated the application of the method to nearby region hosting recently discovered loose structures – tidal tails of Coma Ber star cluster and a nearby stellar group named Group X. We compare the results of straightforward clustering of stellar data with results of using our met

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Guo, Runxia, Na Zhang, Jiaqi Wang, and Jiankang Dong. "Phase partition and identification based on a two-step method for batch process." Transactions of the Institute of Measurement and Control 40, no. 16 (2018): 4472–83. http://dx.doi.org/10.1177/0142331217750222.

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The batch process is a batch-repeated production process, which shows a multiple modal switching within the batch. This makes it difficult to use a single-mode analysis method to achieve accurate modeling and fault diagnosis. Therefore, a novel two-step phase partition idea is proposed based on improved affinity propagation (AP) clustering and sub-phase similarity diminishing scan (PSDS) method. In order to capture the dynamic characteristics of the modes switching, the improved AP clustering is used for phase preliminary partition, in which an effective method that is more suitable for comple

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Lei, Xiujuan, Fang-Xiang Wu, Jianfang Tian, and Jie Zhao. "ABC and IFC: Modules Detection Method for PPI Network." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/968173.

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Many clustering algorithms are unable to solve the clustering problem of protein-protein interaction (PPI) networks effectively. A novel clustering model which combines the optimization mechanism of artificial bee colony (ABC) with the fuzzy membership matrix is proposed in this paper. The proposed ABC-IFC clustering model contains two parts: searching for the optimum cluster centers using ABC mechanism and forming clusters using intuitionistic fuzzy clustering (IFC) method. Firstly, the cluster centers are set randomly and the initial clustering results are obtained by using fuzzy membership

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Lee, Mingyung, Seonghun Lee, Jaehwa Park, and Seongwon Seo. "Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm." Animals 10, no. 8 (2020): 1348. http://dx.doi.org/10.3390/ani10081348.

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The aim of the study was to group the lactation curve (LC) of Holstein cows in several clusters based on their milking characteristics and to investigate physiological differences among the clusters. Milking data of 330 lactations which have a milk yield per day during entire lactation period were used. The data were obtained by refinement from 1332 lactations from 724 cows collected from commercial farms. Based on the similarity measures, clustering was performed using the k-medoids algorithm; the number of clusters was determined to be six, following the elbow method. Significant differences

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Zhang, Wei, Han Hu Wang, and Hui Li. "KSR-Tree: A Clustering Based High-Dimensional Indexing Approach." Applied Mechanics and Materials 411-414 (September 2013): 366–69. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.366.

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In high-dimensional index, overflow split has been verified to be critical to the performance of kNN query processing. A node is split into two parts in the traditional way, however, these methods tend to result in many overlap regions in high-dimensional spaces which will significantly degrade the performance of retrieval. In this paper, we propose a method named KSR-Tree, it making use of a clustering based split algorithm to divides the node into multiple parts, and most of overlap regions will guarantee to be placed into the same node. This approach not only increased the capacity for newl

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Jin, Yizhong, and Ya Cheng. "A Method of Urban Wind Field Visualization Based on Deep Learning." Academic Journal of Science and Technology 5, no. 2 (2023): 225–27. http://dx.doi.org/10.54097/ajst.v5i2.6982.

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In order to solve the problems of incomplete feature extraction, visual results that disrupt the continuity of the flow field, and unstable clustering resulting in poor streamline representation during urban wind field visualization, a three-dimensional streamline visualization method based on deep learning was proposed. This method consists of two parts: one is streamline feature learning, and the other is clustering method. The Euclidean distance represented by the streamline is used as the similarity between the streamlines for clustering, and the clustering results obtained are weighted an

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Al-Asadi, Samraa, and Safaa Al-Mamory. "Improved BAT Algorithm Using Density-Based Clustering." Inteligencia Artificial 26, no. 72 (2023): 102–23. http://dx.doi.org/10.4114/intartif.vol26iss72pp102-123.

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BAT algorithm is a nature-inspired metaheuristic algorithm that depends on the principle of the echolocation behavior of bats. However, the algorithm suffers from being stuck in the local optima early due to its poor exploration. An improved BAT algorithm based on the density-based clustering technique is proposed to enhance the algorithm’s performance. In this paper, the initial population is improved by generating two populations, randomly and depending on the clusters’ center information, and by getting the fittest individuals from these two populations, the initial improved one is ge

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Zhang, Ziyue, Yiwei Zhao, Minghui Bu, Wenxiu Ye, and Huiheng Zhang. "Research on the Whole Rice Industry Chain Model from the Perspective of Rural Revitalization: Taking Qianshan City as an Example." Frontiers in Humanities and Social Sciences 3, no. 4 (2023): 68–76. http://dx.doi.org/10.54691/fhss.v3i4.4755.

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The report of the 20th National Congress of the Communist Party of China proposes to comprehensively promote rural revitalization, adhere to the priority development of agriculture and rural areas, and accelerate the construction of an agricultural power. Studying the supply chain of agricultural products is one of the ways to achieve rural revitalization, with the aim of providing agricultural products to consumers quickly and safely, while reducing costs in the supply chain and improving economic benefits. Rice, as one of the most important food crops, feeds over half of the population. Rese

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Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.

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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feat

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Marcheggiani, Diego, and Ivan Titov. "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations." Transactions of the Association for Computational Linguistics 4 (December 2016): 231–44. http://dx.doi.org/10.1162/tacl_a_00095.

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We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization mod

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KRAMER, OLIVER, and HOLGER DANIELSIEK. "A CLUSTERING-BASED NICHING FRAMEWORK FOR THE APPROXIMATION OF EQUIVALENT PARETO-SUBSETS." International Journal of Computational Intelligence and Applications 10, no. 03 (2011): 295–311. http://dx.doi.org/10.1142/s1469026811003112.

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In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space — also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different characteristics. In this work, we propose a clustering-based niching framework for multi-objective population-based approaches that allows to approximate equivalent Pareto-subsets. Iteratively, the clusteri

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Asgarnezhad, Razieh, Safaa Saad Abdull Majeed, Zainab Aqeel Abbas, and Sarah Sinan Salman. "Providing an Efficient Method to Identify Structural Balanced Social Network Charts using Data Mining Techniques." Wasit Journal of Computer and Mathematics Science 1, no. 1 (2022): 49–59. http://dx.doi.org/10.31185/wjcm.vol1.iss1.22.

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As social communications become widespread, social networks are expanding day by day, and the number of members is increasing. In this regard, one of the most important issues on social networks is the prediction of the link or the friend's suggestion, which is usually done using similarities among users. In the meantime, clustering methods are very popular, but because of the high convergence velocity dimensions, clustering methods are usually low. In this research, using spectral clustering and diminishing dimensions, reducing the amount of information, reduces clustering time and reduces co

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Ponda, Hendry, and Uci Rahmalisa. "The Relationship Between Age, Parity, Ideal Weight, and Blood Pressure in Diagnosing Hypertension in Pregnant Women Using The K-Means Algorithm." JURNAL TEKNOLOGI DAN OPEN SOURCE 6, no. 2 (2023): 1–9. http://dx.doi.org/10.36378/jtos.v6i2.3157.

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Hypertension is one of the health problems that often arise during pregnancy and can cause complications in 2-3% of pregnancies. Hypertension In Pregnancy (HDK) is defined as a blood pressure of ≥140/90 mmHg in two or more measurements. Data mining is a combination of a number of computer science disciplines that is defined as the process of discovering new patterns from very large data sets. By looking at records on Age, IMT, Parity / Gravidity, and Blood Pressure and analysis with K-Means clustering, it can be seen that the similarity of values of the above variables ultimately forms pattern

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Osipov, Pavel, and Arkady Borisov. "Practice of Web Data Mining Methods Application." Scientific Journal of Riga Technical University. Computer Sciences 40, no. 1 (2009): 101–7. http://dx.doi.org/10.2478/v10143-010-0014-x.

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Practice of Web Data Mining Methods ApplicationRecent growth of information on the Internet imposes high demands on the effectiveness of processing algorithms. This paper discusses some algorithms from the field of Web Data Mining which have proved effective in many existing applications. The paper is divided into two logical parts; the first part provides a theoretical description of the algorithms, but the second one contains examples of their successful use to solve real problems. Search algorithms of vague duplicates of documents are currently actively used by all the leading search engine

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