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Articles de revues sur le sujet « Model-based Cluster »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 19 juillet 2025

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1

Endo, Yasunori, Ayako Heki, and Yukihiro Hamasuna. "Non Metric Model Based on Rough Set Representation." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (2013): 540–51. http://dx.doi.org/10.20965/jaciii.2013.p0540.

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The non metricmodel is a kind of clustering method in which belongingness or the membership grade of each object in each cluster is calculated directly from dissimilarities between objects and in which cluster centers are not used. The clustering field has recently begun to focus on rough set representation instead of fuzzy set representation. Conventional clustering algorithms classify a set of objects into clusters with clear boundaries, that is, one object must belong to one cluster. Many objects in the real world, however, belong to more than one cluster because cluster boundaries overlap

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Duan, Guiqin, and Chensong Zou. "A clustering effectiveness measurement model based on merging similar clusters." PeerJ Computer Science 10 (February 29, 2024): e1863. http://dx.doi.org/10.7717/peerj-cs.1863.

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This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the “rough clustering” process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two

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Banerjee, Saibal, and Azriel Rosenfeld. "Model-based cluster analysis." Pattern Recognition 26, no. 6 (1993): 963–74. http://dx.doi.org/10.1016/0031-3203(93)90061-z.

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Stahl, Daniel, and Hannah Sallis. "Model-based cluster analysis." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 4 (2012): 341–58. http://dx.doi.org/10.1002/wics.1204.

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Huang, He, and Hui Xiao. "Internet Industry Cluster Design Based on PDE Mathematical Model." Applied Mechanics and Materials 539 (July 2014): 959–63. http://dx.doi.org/10.4028/www.scientific.net/amm.539.959.

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The industrial cluster is formed by the common competitiveness elements of enterprise group. Under the cluster environment, common technology and common customer as well as distribution channel are composition of cluster development performance mode. On the basis of the parabolic PDE cluster development model, and combined with Internet industrial cluster analysis of virtual platform, the Internet structure industrial cluster analysis system is designed. In order to verify the validity and reliability of the model and system, this paper takes the cluster development of machining as an example

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Younghwan Kim, Younghwan Kim, and Huy Kang Kim Younghwan Kim. "Cluster-based Deep One-Class Classification Model for Anomaly Detection." 網際網路技術學刊 22, no. 4 (2021): 903–11. http://dx.doi.org/10.53106/160792642021072204017.

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Fang, Yong Heng, and Jing Yi Yi. "Study on Evolution Mechanism of Industrial Cluster Based on Brusselator Model." Applied Mechanics and Materials 687-691 (November 2014): 4832–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.4832.

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The article using Brusselator model analyses the evolution mechanism of industrial clusters. The study found, the formation of industrial clusters is an inner reinforcing cycle accumulation process, the competing interaction is an important condition for the evolution of industrial clusters, and cluster innovation driving the system to the state development more orderly, form the new dissipative structure, promote the evolution of industrial cluster.

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Meghana, J., J. Hanumanthappa, S. P. Shiva Prakash, and Kiril Krinkin. "Relationship-Cluster Head Selection and Data Compression Enabled Cluster-Based Aggregation Model for Social Internet of Things." Indian Journal Of Science And Technology 16, no. 41 (2023): 3605–16. http://dx.doi.org/10.17485/ijst/v16i41.1256.

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Lim, Michael K., and So Young Sohn. "Cluster-based dynamic scoring model." Expert Systems with Applications 32, no. 2 (2007): 427–31. http://dx.doi.org/10.1016/j.eswa.2005.12.006.

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Lahoorpoor, Bahman, Hamed Faroqi, Abolghasem Sadeghi-Niaraki, and Soo-Mi Choi. "Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem." Sustainability 11, no. 11 (2019): 3205. http://dx.doi.org/10.3390/su11113205.

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on

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Kim, Eun Young, Young Yoon, Ha Yeon Park, and Sang Hee Seo. "Development of a parental competency model for competency-based parent education." Association for Studies in Parents and Guardians 12, no. 1 (2025): 51–78. https://doi.org/10.56034/kjpg.2025.12.1.51.

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This study aims to develop a competency model for competency-based parent education. Through conducting a comprehensive literature review, focus group interviews (FGI), and Delphi surveys, a parental competency model consisting of five competency clusters, 16 core competencies, and 40 sub-competencies was established. The model identifies five primary competency clusters: self-care, basic parenting, child education, school collaboration, and support for child independence. The self-care cluster includes self-understanding and reflection, self-management and growth, and social relationship buil

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Xi, Yaoyi, Gang Chen, Bicheng Li, and Yongwang Tang. "Topic Evolution Analysis Based on Cluster Topic Model." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 1 (2016): 66–75. http://dx.doi.org/10.20965/jaciii.2016.p0066.

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Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two

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Li, Wei, and Mei An Li. "A Text Clustering Algorithms Based on Hidden Markov Model." Applied Mechanics and Materials 135-136 (October 2011): 1155–58. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.1155.

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Based on the probability model of clustering algorithm constructs a model for each cluster, calculate probability of every text falls in different models to decide text belongs to which cluster, conveniently in global Angle represents abstract structure of clusters. In this paper combining the hidden Markov model and k - means clustering algorithm realize text clustering, first produces first clustering results by k - means algorithm, as the initial probability model of a hidden Markov model ,constructed probability transfer matrix prediction every step of clustering iteration, when subtractio

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Zhang, Zhan, Rong Huang, and Zhenglong Li. "Energy Prediction Model of PSO-BP Neural Network Three-dimensional Clusters based on Atomic Coordinates." Scholars Journal of Physics, Mathematics and Statistics 8, no. 6 (2021): 118–22. http://dx.doi.org/10.36347/sjpms.2021.v08i06.002.

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Energy prediction for different cluster structures is the basis for finding and predicting the global optimal structure of clusters. The current methods for predicting the energy of the ground state structures of different clusters include theoretical prediction methods and optimized simplified potential energy function methods. The accuracy of the theoretical prediction method is high, but its calculation amount is too large. Therefore, this paper proposes a PSO-BP neural network three-dimensional cluster energy prediction model based on atomic coordinates, and uses different types of Euclide

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Teo, Boon K., and Hong Zhang. "Cluster of clusters (C2) model for electron counting of supracluster based on smaller cluster units." Inorganica Chimica Acta 144, no. 2 (1988): 173–76. http://dx.doi.org/10.1016/s0020-1693(00)86282-9.

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Rahayu, Riski Sayuti, Yunastiti Purwaningsih, and Akhmad Daerobi. "Mapping Of Provincial Food Security In Indonesia Using Based Clustering Model." Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan 20, no. 1 (2019): 69–79. http://dx.doi.org/10.23917/jep.v20i1.7096.

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Indonesia was known as an agrarian and maritime country, should not experience difficulties in fulfill food needs or having high food security. However, it is a formidable challenge for the Indonesia to meeting food needs. The low level of food security was caused more by Indonesia's geographical conditions in the form of islands that cause inequality of food production, distribution and absorption among provinces in Indonesia. To reduce the occurrence of food security inequality between provinces in Indonesia, clusters was formed based on food security indicators. Based clustering technique i

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p, p., p. p, p. p, p. p, p. p, and p. p. "Analysis of Competitiveness in the Domestic Semiconductor Cluster Using Entropy Technique: Based on the GEM-ESG Model." International Academy of Global Business and Trade 20, no. 1 (2024): 81–106. http://dx.doi.org/10.20294/jgbt.2024.20.1.81.

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Purpose - The study aims to conduct an empirical analysis of semiconductor cluster integrated competitiveness with a focus on the GEM-ESG model. Specifically, the study seeks to compare and analyze the development factors of semiconductor clusters in different regions. The results of the empirical study will reveal a clear concentration phenomenon in the semiconductor industry clusters of some regions. Design/Methodology/Approach - Previous research has established the factors influencing the competitive advantage of industrial clusters. The study collected competitive indicators from ma

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Jang, Jaewon, and David B. Hitchcock. "Model-Based Cluster Analysis of Democracies." Journal of Data Science 10, no. 2 (2021): 297–319. http://dx.doi.org/10.6339/jds.201204_10(2).0009.

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Qin, Sun Tao, and Wei Wei Fu. "Evolvement Model of Eco-Industrial Cluster - Research Based on Complex Adaptive System." Advanced Materials Research 304 (July 2011): 247–52. http://dx.doi.org/10.4028/www.scientific.net/amr.304.247.

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By using the theory of complex adaptive system(CAS), a searching analysis of Eco-Industrial Cluster was put forward by the strategy of control, organization and evolvement,then proved that Eco-Industrial Cluster system is a real complex adaptive system(CAS), a relevant concept model was built up, and then a dynamic simulation modeling for Eco-Industrial Cluster also constructed on SWARM platform. By researching the complexity, creativity, learning and adaptability of the system, the author was trying to build up a new theory and practice method for both programmers and managers of Eco-Industri

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Honda, Katsuhiro, Yoshiki Hakui, Seiki Ubukata, and Akira Notsu. "A Heuristic-Based Model for MMMs-Induced Fuzzy Co-Clustering with Dual Exclusive Partition." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 1 (2020): 40–47. http://dx.doi.org/10.20965/jaciii.2020.p0040.

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MMMs-induced fuzzy co-clustering achieves dual partition of objects and items by estimating two different types of fuzzy memberships. Because memberships of objects and items are usually estimated under different constraints, the conventional models mainly targeted object clusters only, but item memberships were designed for representing intra-cluster typicalities of items, which are independently estimated in each cluster. In order to improve the interpretability of co-clusters, meaningful items should not belong to multiple clusters such that each co-cluster is characterized by different rep

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Liping, Zhang, and Yang Huiya. "Research on Innovation Performance of VR and Tobacco Industrial Cluster Based on Structural Equation Model." Tobacco Regulatory Science 7, no. 6 (2021): 5755–69. http://dx.doi.org/10.18001/trs.7.6.58.

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As a traditional industry, the tobacco industry is an important part of the national economy and has an important position in meeting social consumption demand and increasing national and local fiscal revenue. And VR industry, as an emerging industrial economy, can effectively empower the development of tobacco industry. To further promote the development of VR and tobacco industry clusters and optimize the industrial structure, this paper constructs a conceptual model of the factors influencing the innovation performance of VR and tobacco industry clusters from a social network perspective ba

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Hendrawati, Triyani, Aji Hamim Wigena, I. Made Sumertajaya, Bagus Sartono, Anindya Apriliyanti Pravitasari, and Mohammad Hamid Asnawi. "The ensemble distance on model-based clustering for regions clustering based on rainfall: The case of rainfall in West Java Indonesia." International Journal of Data and Network Science 8, no. 2 (2024): 1187–96. http://dx.doi.org/10.5267/j.ijdns.2023.11.015.

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Time series data clusters are being researched thoroughly. The distance metric drives the development of the clustering time series. The ARIMA model is one of the models that can be employed in model-based clustering, although differing model selection criteria can lead to uncertainty in the model. In this investigation, we created a technique for ensemble distance-based time series data clustering. To express the distance between two series, five distances based on the five model selection criteria are utilized. The average of the five distances reflects the distance of two time series data.

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Raditya, Muhammad Hafidh, Indwiarti, and Aniq Atiqi Rohmawati. "House Prices Segmentation Using Gaussian Mixture Model-Based Clustering." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 5 (2022): 866–71. http://dx.doi.org/10.29207/resti.v6i5.4459.

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House is a place for humans to live and a main necessity for humans. For years, the need for houses is increasing and varied so that it affects the selling price of the house. Therefore, more research is needed to learn about the selling price of houses. This research is only focusing on house price segmentation in DKI Jakarta using the Gaussian Mixture Model-Based Clustering Method with the Expectation-Maximization algorithm. The goal of this research is to make a house price segmentation model so that we can obtain useful information for the potential buyer. Clustering with GMM utilize the l

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Ali, Zain Anwar, Zhangang Han, and Rana Javed Masood. "Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture." Sensors 21, no. 11 (2021): 3820. http://dx.doi.org/10.3390/s21113820.

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This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the lead

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Sun, Yeran, Yu Wang, Ke Yuan, Ting On Chan, and Ying Huang. "Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection." Sustainability 12, no. 20 (2020): 8681. http://dx.doi.org/10.3390/su12208681.

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Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connect

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Palčič, Iztok. "Industrial clusters development and organisation model." Anali PAZU 3, no. 1 (2022): 26–33. http://dx.doi.org/10.18690/analipazu.3.1.26-33.2013.

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Industrial clusters have been a prevalent element of several national competitiveness policies for the last 15 years The author of this paper has followed the birth, organisation and performance of industrial clusters in Slovenia and Austria for the period of three years. Based on several in-depth case studies in Slovenia and Austria I have built a cluster development and organisation model applicable to smaller (transitional) countries. I have identified factors that have an impact on cluster development and organisation at the level of general business environment. At the same time I have id

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Ji, Ming, Fei Wang, Jia Ning Wan, and Yuan Liu. "Literature Review on Hidden Markov Model-Based Sequential Data Clustering." Applied Mechanics and Materials 713-715 (January 2015): 1750–56. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1750.

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The purpose of this report is to investigate current existing algorithm to cluster sequential data based on hidden Markov model (HMM). Clustering is a classic technique that divides a set of objects into groups (called clusters) so that objects in the same cluster are similar in some sense. The clustering of sequential or time series data, however, draws lately more and more attention from researchers. Hidden Markov model (HMM)-based clustering of sequences is probabilistic model-based approach to clustering sequences. Generally, there are two kinds of methodologies: parametric and semi-parame

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Wang, Xianda, Yaqi Qiao, Duo Wu, Chenrui Wu, and Fangxin Wang. "Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21269–77. https://doi.org/10.1609/aaai.v39i20.35426.

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In recent years, the distributed training of foundation models (FMs) has seen a surge in popularity. In particular, federated learning enables collaborative model training among edge clients while safeguarding the privacy of their data. However, federated training of FMs across resource-constrained and highly heterogeneous edge devices encounter several challenges. These include the difficulty of deploying FMs on clients with limited computational resources and the high computation and communication costs associated with fine-tuning and collaborative training. To address these challenges, we p

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Yang, Qiong. "Study on The Industrial Cluster of Tropical Bananas Based on Gem Model." Acta Universitatis Cibiniensis. Series E: Food Technology 21, no. 1 (2017): 69–74. http://dx.doi.org/10.1515/aucft-2017-0008.

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Abstract In recent years, the development of agricultural industry clusters is rapid in China. As a main producing area of bananas, the Hainan Ledong Industrial Cluster’s competitiveness is of great significance to the development of the whole banana industry in China. This paper first analyzed the cultivation of tropical banana and the market share of bananas in each region, and then analyzed the competitiveness of Ledong banana industry cluster through the GEM (Groundings- Enterprises- Markets) model. The results showed that the GEM model score was 456 points, and the domestic cluster compet

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Fan, Ru Guo, and Hong Juan Zhang. "Research on the Low-Carbon Evolutionary Model of Chinese Traditional Industrial Clusters Based on Evolutionary Games Theory under Low-Carbon Constraints." Applied Mechanics and Materials 448-453 (October 2013): 4461–64. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.4461.

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The low-carbon evolution of traditional industry cluster is the key to a low-carbon economy, and also a frontier of industry cluster theory research. The paper uses evolutionary game theory to construct a low-carbon evolutionary model of Chinese traditional industrial clusters, which considers uncertain factors such as political, economic, cultural, etc. Through the analysis of the cluster low-carbon evolutionary paths and stable equilibrium strategies, the model reflects the inherent law of clusters low-carbon evolution. Finally, the paper gives advices to promote industrial cluster agents to

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Mistry, Sejal, Ramkiran Gouripeddi, Candace M. Reno, Samir Abdelrahman, Simon J. Fisher, and Julio C. Facelli. "Detecting hypoglycemia-induced electrocardiogram changes in a rodent model of type 1 diabetes using shape-based clustering." PLOS ONE 18, no. 5 (2023): e0284622. http://dx.doi.org/10.1371/journal.pone.0284622.

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Sudden death related to hypoglycemia is thought to be due to cardiac arrhythmias. A clearer understanding of the cardiac changes associated with hypoglycemia is needed to reduce mortality. The objective of this work was to identify distinct patterns of electrocardiogram heartbeat changes that correlated with glycemic level, diabetes status, and mortality using a rodent model. Electrocardiogram and glucose measurements were collected from 54 diabetic and 37 non-diabetic rats undergoing insulin-induced hypoglycemic clamps. Shape-based unsupervised clustering was performed to identify distinct cl

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Husein, Amir Mahmud, Februari Kurnia Waruwu, Yacobus M. T. Batu Bara, Meleyaki Donpril, and Mawaddah Harahap. "Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors." SinkrOn 6, no. 1 (2021): 137–43. http://dx.doi.org/10.33395/sinkron.v6i1.11191.

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Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analy

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Fraley, C. "How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis." Computer Journal 41, no. 8 (1998): 578–88. http://dx.doi.org/10.1093/comjnl/41.8.578.

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Azhar, Muhammad, Mark Junjie Li, and Joshua Zhexue Huang. "A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data." Entropy 21, no. 9 (2019): 906. http://dx.doi.org/10.3390/e21090906.

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Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first

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Chiu, Stephen L. "Fuzzy Model Identification Based on Cluster Estimation." Journal of Intelligent and Fuzzy Systems 2, no. 3 (1994): 267–78. http://dx.doi.org/10.3233/ifs-1994-2306.

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Evans, Katie, Tanzy Love, and Sally W. Thurston. "Outlier Identification in Model-Based Cluster Analysis." Journal of Classification 32, no. 1 (2015): 63–84. http://dx.doi.org/10.1007/s00357-015-9171-5.

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Fraley, Chris, and Adrian E. Raftery. "MCLUST: Software for Model-Based Cluster Analysis." Journal of Classification 16, no. 2 (1999): 297–306. http://dx.doi.org/10.1007/s003579900058.

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Rapley, V. E., and A. H. Welsh. "Model-based inferences from adaptive cluster sampling." Bayesian Analysis 3, no. 4 (2008): 717–36. http://dx.doi.org/10.1214/08-ba327.

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S, Divya, and Sripriya N. "SEMANTIC BASED EXTRACTIVE DOCUMENT SUMMARIZATION USING DEEP LEARNING MODEL." ICTACT Journal on Soft Computing 15, no. 4 (2025): 3669–81. https://doi.org/10.21917/ijsc.2025.0509.

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The rapid growth of web documents led to the entailment of automatic document summaries. Extractive summarization designates certain principle features from the input document and groups them together to generate a summary. This empowers readers to quickly browse the document and unveil the information in it. The focus of this work is to propose a clustering algorithm that suits for the summarization of both Tamil and English documents. Transformer mechanism that is trained on 104 languages (which includes Tamil and English language) is used to represent each sentence in the source document as

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Zhang, Yuan Yuan, and Fu Zhou Luo. "Industrial Cluster Competitiveness Evaluation Model Research Based on Entropy Weight TOPSIS Method." Applied Mechanics and Materials 584-586 (July 2014): 2676–80. http://dx.doi.org/10.4028/www.scientific.net/amm.584-586.2676.

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Existing competitiveness evaluation methods of industrial clusters is too subjective and can’t be a true reflection of its core competencies; evaluation index is not uniform and can’t form a competitiveness evaluation index system. We took non-ferrous metal industry cluster of Shaanxi Province as an example, built a competitive assessment model of industrial clusters from scale, market, innovation, and efficiency competitiveness. We used Entropy-TOPSIS method to analyze. The results show that Entropy-TOPSIS method is more objective and matches the actual development in the evaluation of indust

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Zhang, Chunyue, Tiejun Zhao, and Tingting Li. "A Dirichlet Process Mixture Based Name Origin Clustering and Alignment Model for Transliteration." Advances in Artificial Intelligence 2015 (July 29, 2015): 1–10. http://dx.doi.org/10.1155/2015/927063.

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In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatical

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Collin, Sven-Olof. "Cluster governance of School-university clusters." New Collegium 2, no. 100 (2020): 25–29. http://dx.doi.org/10.30837/nc.2020.2.25.

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University-School clusters: Best practices and the prospects for their adaptation to Ukrainian context : the XVIII International Scientific and Practical Conference (14-th Febuary 2020, Kharkiv Univ. of Humanities “People’s Ukrainian Acad”. The proceedings of the XVIII International Scientific and Practical Conference “University-School clusters: include a variety of articles on the issues of the formation of a cluster-based educational model and its role in the development of the educational space.Covered are the essence of university-school clusters, the conceptual framework for their

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Oyelade, Jelili, Itunuoluwa Isewon, Damilare Olaniyan, Solomon O. Rotimi, and Jumoke Soyemi. "Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data." F1000Research 6 (September 19, 2017): 1706. http://dx.doi.org/10.12688/f1000research.12360.1.

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Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determ

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Oyelade, Jelili, Itunuoluwa Isewon, Damilare Olaniyan, Solomon O. Rotimi, and Jumoke Soyemi. "Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data." F1000Research 6 (May 25, 2018): 1706. http://dx.doi.org/10.12688/f1000research.12360.2.

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Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determ

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Forbes, Owen, Edgar Santos-Fernandez, Paul Pao-Yen Wu, et al. "clusterBMA: Bayesian model averaging for clustering." PLOS ONE 18, no. 8 (2023): e0288000. http://dx.doi.org/10.1371/journal.pone.0288000.

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Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one ‘best’ model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic inte

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Shkoda, Mariana. "MANAGEMENT MODEL OF BUSINESS DEVELOPMENT BASED ON CLUSTER CONSTRAINS." Journal of Strategic Economic Research, no. 2 (October 5, 2022): 105–12. http://dx.doi.org/10.30857/2786-5398.2022.2.10.

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The main goal of this work is to investigate the management model of business development based on cluster constrains. The article uses general scientific research methods, in particular, empirical methods to describe the basic approaches to modeling business development management in cluster conditions, theoretical methods, in particular, the classification of the concept of business development management on the basis of cluster partnership, etc. The article explores approaches to modeling business development management based on cluster partnerships. Four basic approaches are distinguished:

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Ding, Wanying, Youlin Gu, Yihua Hu, Guolong Chen, Hao Cao, and Haihao He. "Ballistic cluster–cluster aggregation model optimization." AIP Advances 13, no. 3 (2023): 035017. http://dx.doi.org/10.1063/5.0123360.

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In this paper, an optimized model based on the ballistic cluster–cluster aggregation model is proposed to study the optical properties of aggregated particle structures. The critical improvement of the optimized model is the ability to arbitrarily select the original number of particles in the simulation and set different sizes of particles, whereas the original model is limited to 2 n particles. Herein, the discrete dipole approximation method was used to calculate the optical extinction properties of the aggregation structure. First, the effect of porosity, which is a significant parameter,

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Haslbeck, Jonas M. B., and Dirk U. Wulff. "Estimating the number of clusters via a corrected clustering instability." Computational Statistics 35, no. 4 (2020): 1879–94. http://dx.doi.org/10.1007/s00180-020-00981-5.

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Abstract We improve instability-based methods for the selection of the number of clusters k in cluster analysis by developing a corrected clustering distance that corrects for the unwanted influence of the distribution of cluster sizes on cluster instability. We show that our corrected instability measure outperforms current instability-based measures across the whole sequence of possible k, overcoming limitations of current insability-based methods for large k. We also compare, for the first time, model-based and model-free approaches to determining cluster-instability and find their performa

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Borisova, N. G., T. A. Segeda, and M. T. Tumenbaeva. "Thermophysical and thermodynamic properties of substances in the gas phase – the basis of the cluster model." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 1, no. 79 (2021): 81–88. http://dx.doi.org/10.47533/2020.1606-146x.65.

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The research is devoted to calculated-theoretical and applied analysis of thermo physical and thermodynamic properties of substances in gaseous phase used in heat-power engineering basing on cluster model. It is impossible to make measurements under all conditions that can be in heat-power engineering practice, so the theory is required that is based on reliable model. Such a model is considered to be a molecular-cluster model and computation schemes have been developed within its frameworks. These are schemes for computation of thermodynamic properties of substances in gaseous phase. The rese

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IRWAN, IRWAN, ASTRI YUNI HASHARI, HISYAM IHSAN, and AHMAD ZAKI. "PENGGUNAAN SELF ORGANIZING MAP DALAM PENGELOMPOKAN TINGKAT KESEJAHTERAAN MASYARAKAT." Jambura Journal of Probability and Statistics 1, no. 2 (2020): 57–68. http://dx.doi.org/10.34312/jjps.v1i2.7266.

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Self Organizing Map (SOM) is one of the topology forms of Unsupervised Neural Network where in the learning process does not require output target. Clusters in this research consist of one or more regency/city areas that have certain characteristics based on the variables. Each cluster had to be validated by using the Davies Bouldin Index value to get the best cluster formation from the SOM algorithm learning process. The best cluster model is the cluster model that has the smallest Davies Bouldin Index value. This research used 30 variables that refer to the key statistics of South Sulawesi P

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