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Artykuły w czasopismach na temat „Hierarchical cluster”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Elkahlout, Gamal R., and Mohaned A. Elkahlout. "Employing cluster analysis in defining groundwater wells patterns in Rafah." Edelweiss Applied Science and Technology 8, no. 6 (2024): 3542–55. http://dx.doi.org/10.55214/25768484.v8i6.2753.

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Clustering analysis techniques used in identifying homogeneity of groundwater wells patterns in terms of Chloride, Nitrate, and TDS. Data was collected through the laboratories of Palestinian Water Authority in the Rafah 2020. R-Programming is used for data analysis. Kolmogorov-Smirnov test is used to test data normality. Hierarchical clustering analysis applied to generate a cluster tree (r>0.75) in correlation matrix. Agglomerative Hierarchical Clustering is used to classify with “average” method and found that there are two clusters. First cluster with 28 wells and Second cluster with 9

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Huang, Jinke, Xiaoguang Fan, Xin Xiang, Min Wan, Zhenfu Zhuo, and Yongjian Yang. "A Clustering Routing Protocol for Mobile Ad Hoc Networks." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5395894.

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The dynamic topology of a mobile ad hoc network poses a real challenge in the design of hierarchical routing protocol, which combines proactive with reactive routing protocols and takes advantages of both. And as an essential technique of hierarchical routing protocol, clustering of nodes provides an efficient method of establishing a hierarchical structure in mobile ad hoc networks. In this paper, we designed a novel clustering algorithm and a corresponding hierarchical routing protocol for large-scale mobile ad hoc networks. Each cluster is composed of a cluster head, several cluster gateway

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Godara, Poonam, Shrawan Kumar, and Darvinder Kumar. "Evaluation of Genetic Variation in Indian mustard (Brassica Juncea L Czern and Coss) Using Multivariate Techniques." Journal of Agriculture Research and Technology 47, no. 03 (2022): 344–48. http://dx.doi.org/10.56228/jart.2022.47315.

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A set of 310 lines of Indian mustard (Brassica juncea L Czern and Coss) were analysed for cluster and principal component analysis (PCA). PCA identified four principal components which explained 65.13% of total variability among the 310 genotypes. Hierarchical cluster analysis grouped 310 genotypes into 3 clusters. Cluster1 included maximum number of 155 genotypes and clusters 3 had the lowest number of 43 genotypes. The grouping pattern of genotypes obtained by cluster analysis and PCA plots was almost similar.

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Muhammad Akmal Hafiz Abidin, Satria Raditya Nugroho, and Sri Pingit Wulandari. "Pengelompokan Kabupaten/Kota Berdasarkan Indeks Pembangunan Manusia Provinsi Jawa Timur Tahun 2023." Jurnal Cakrawala Akademika 1, no. 4 (2024): 1143–57. https://doi.org/10.70182/jca.v1i4.10.

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Quality human resources are the key to success in achieving goals, both individual and regional. One of the main indicators of the quality of human resources is the Human Development Index (HDI), which includes poverty, life expectancy, unemployment, education, and labor participation. East Java Province, with its diversity and large population, faces great challenges in improving the quality of human resources. One method of measuring whether these factors affect HDI is by dividing clusters based on the region, then analyzed using cluster analysis. Cluster analysis, both hierarchical and non-

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Avatavului, Cristian, and Costin-Anton Boiangiu. "A Hierarchical Cluster Tree Approach Leveraging Delaunay Triangulation." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 3 (2023): 408–33. http://dx.doi.org/10.18662/brain/14.3/482.

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This research introduces a robust and reliable technique for structuring document image pages hierarchically, harnessing the power of Delaunay triangulation. Central to our approach is the formation of a cluster tree, which encapsulates the page's content through strategically exploiting layout elements arrangements and their relative distances. By applying our technique, we proficiently categorize the page into distinct clusters encompassing images, titles, and paragraphs. The consequent hierarchical framework, founded on the cluster tree, establishes a durable and trustworthy blueprint of th

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Schonlau, Matthias. "The Clustergram: A Graph for Visualizing Hierarchical and Nonhierarchical Cluster Analyses." Stata Journal: Promoting communications on statistics and Stata 2, no. 4 (2002): 391–402. http://dx.doi.org/10.1177/1536867x0200200405.

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In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propose an alternative graph called a “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for nonhierarchical clustering algorithms such as k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical. I present the Stata code and give two examples.

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Bergamini, P., A. Agnello, and G. B. Caminha. "Cluster strong lensing with hierarchical inference." Astronomy & Astrophysics 648 (April 2021): A123. http://dx.doi.org/10.1051/0004-6361/201937138.

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Context. Lensing by galaxy clusters is a versatile probe of cosmology and extragalactic astrophysics, but the accuracy of some of its predictions is limited by the simplified models adopted to reduce the (otherwise intractable) number of degrees of freedom. Aims. We aim to explore cluster lensing models in which the parameters of all cluster member galaxies are free to vary around some common scaling relations with non-zero scatter and deviate significantly from these relations if, and only if, the data require this. Methods. We devised a Bayesian hierarchical inference framework that enables

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Fauziyah, Wardah Muna, and Anneke Iswani Achmad. "Penerapan Analisis Cluster Hybrid untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Barat Berdasarkan Indikator Kemiskinan Tahun 2022." Bandung Conference Series: Statistics 3, no. 2 (2023): 566–74. http://dx.doi.org/10.29313/bcss.v3i2.8610.

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Abstract. Hybrid cluster analysis is a combination of hierarchical and non-hierarchical clusters, which has a goal as an alternative method. The advantage of hybrid cluster analysis is that it can determine k-clusters for the process of making non-hierarchical clusters through the results of making hierarchical cluster methods, which will produce the right k-clusters. With the advantages of the hybrid cluster analysis, this research will combine the single linkage method with k-means, then the ward method with k-means. The purpose of this study was to determine the grouping of districts/cities

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Ersa Riga Puspita and Mujiati Dwi Kartikasari. "IDENTIFYING THE CLUSTER OF FAMILIES AT RISK OF STUNTING IN YOGYAKARTA USING HIERARCHICAL AND NON-HIERARCHICAL APPROACH." Jurnal Ilmiah Kursor 12, no. 4 (2024): 159–66. https://doi.org/10.21107/kursor.v12i4.358.

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Stunting, or short stature, is a growth disorder usually caused by chronic dietary deficiencies from the prenatal stage to early childhood, typically becoming evident in children after the age of 2. Stunting cases in Yogyakarta Province experienced a decline in 2020. With this development, the government aims to achieve zero stunting in Yogyakarta Province by 2024. To support this goal, a research study was conducted in 2021 to analyze family factors associated with stunting risks in Yogyakarta Province. The study aimed to assist the government in addressing the issue and achieving the target.

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Messakh, Gerald Claudio, Memi Nor Hayati, and Sifriyani Sifriyani. "Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan." EKSPONENSIAL 14, no. 2 (2023): 57. http://dx.doi.org/10.30872/eksponensial.v14i2.1103.

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Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results

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Romesburg, H. Charles. "CLUSTAR-PC: Interactive Program for Hierarchical Cluster Analysis." American Statistician 41, no. 1 (1987): 79. http://dx.doi.org/10.2307/2684333.

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Capra, Miranda G. "Factor Analysis of Card Sort Data: An Alternative to Hierarchical Cluster Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 5 (2005): 691–95. http://dx.doi.org/10.1177/154193120504900512.

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Software and product designers use card sorting to understand item groups and relationships. In the usability community, a common method of formal statistical analysis for open card sort data is hierarchical cluster analysis, which results in a tree of the items sorted into distinct, nested clusters. Hierarchical cluster analysis is appropriate for highly structured settings, like software menus. However, many situations call for softer clusters, such as designing websites where multiple pages link to the same target page. Factor analysis summarizes the categories created in card sorts and gen

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Abghaza Bayu Kusuma Wardhana, Rakha Maheswara, and Sri Pingit Wulandari. "Pengelompokkan Faktor yang Memengaruhi Kemiskinan di Jawa Timur Tahun 2023 Menggunakan Analisis Cluster." Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa 2, no. 6 (2024): 205–27. http://dx.doi.org/10.62383/algoritma.v2i6.304.

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Poverty means the inability to fulfill the basic needs of family members, both food and non-food. In this study, we will analyze several indicators that are assumed to be factors that influence poverty in East Java in 2023, including East Java in 2023, including the percentage of poor people, life expectancy, average years of schooling, and unemployment rate. life expectancy, average years of schooling, and open unemployment rate using cluster analysis to group kabupatens. cluster analysis to group districts/cities into clusters based on the factors that influence poverty. factors that influen

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Syifa Azahra, Zilrahmi, Dodi Vionanda, and Fadhilah Fitri. "K-Modes Analysis with Validation of the DBI in Grouping Provinces in Indonesia based on Indicators of Poor Households." UNP Journal of Statistics and Data Science 2, no. 2 (2024): 173–78. http://dx.doi.org/10.24036/ujsds/vol2-iss2/165.

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One of the most pressing social problems in the world is poverty. One of the efforts to overcome poverty in Indonesia is to group provinces in Indonesia based on indicators of poor households, so that the government can develop poverty reduction strategies according to provincial groups. Cluster analysis is a process of grouping numbers observations based on certain patterns or similarities from these observations. Cluster analysis is divided into two, namely hierarchical and non-hierarchical cluster analysis. Non-hierarchical cluster analysis that can be used is the K-Modes method. K-modes an

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Nasution, Arbi Haza, Yohei Murakami, and Toru Ishida. "Generating similarity cluster of Indonesian languages with semi-supervised clustering." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 531. http://dx.doi.org/10.11591/ijece.v9i1.pp531-538.

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<p>Lexicostatistic and language similarity clusters are useful for computational linguistic researches that depends on language similarity or cognate recognition. Nevertheless, there are no published lexicostatistic/language similarity cluster of Indonesian ethnic languages available. We formulate an approach of creating language similarity clusters by utilizing ASJP database to generate the language similarity matrix, then generate the hierarchical clusters with complete linkage and mean linkage clustering, and further extract two stable clusters with high language similarities. We intr

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Nasution, Arbi Haza, Yohei Murakami, and Toru Ishida. "Generating similarity cluster of Indonesian languages with semi-supervised clustering." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 531–38. https://doi.org/10.11591/ijece.v9i1.pp531-538.

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Lexicostatistic and language similarity clusters are useful for computational linguistic researches that depends on language similarity or cognate recognition. Nevertheless, there are no published lexicostatistic/language similarity cluster of Indonesian ethnic languages available. We formulate an approach of creating language similarity clusters by utilizing ASJP database to generate the language similarity matrix, then generate the hierarchical clusters with complete linkage and mean linkage clustering, and further extract two stable clusters with high language similarities. We introduced an

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Akbari, Ebrahim, Halina Mohamed Dahlan, Roliana Ibrahim, and Hosein Alizadeh. "Hierarchical cluster ensemble selection." Engineering Applications of Artificial Intelligence 39 (March 2015): 146–56. http://dx.doi.org/10.1016/j.engappai.2014.12.005.

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Hamasuna, Yukihiro, Shusuke Nakano, Ryo Ozaki, and and Yasunori Endo. "Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (2019): 577–83. http://dx.doi.org/10.20965/jaciii.2019.p0577.

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The Louvain method is a method of agglomerative hierarchical clustering (AHC) that uses modularity as the merging criterion. Modularity is an evaluation measure for network partitions. Cluster validity measures are also used to evaluate cluster partitions and to determine the optimal number of clusters. Several cluster validity measures are constructed considering the geometric features of clusters. These measures and modularity are considered to be the same concept in the viewpoint of evaluating cluster partitions. In this paper, cluster validity measures based agglomerative hierarchical clus

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Amritkar, R. E. "Stochastic hierarchical model for cluster-cluster aggregation." Physical Review E 60, no. 4 (1999): 4986–89. http://dx.doi.org/10.1103/physreve.60.4986.

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Elmegreen, Bruce G. "The nature and nurture of star clusters." Proceedings of the International Astronomical Union 5, S266 (2009): 3–13. http://dx.doi.org/10.1017/s1743921309990809.

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AbstractStar clusters have hierarchical patterns in space and time, suggesting formation processes in the densest regions of a turbulent interstellar medium. Clusters also have hierarchical substructure when they are young, which makes them all look like the inner mixed parts of a pervasive stellar hierarchy. Young field stars share this distribution, presumably because some of them came from dissolved clusters and others formed in a dispersed fashion in the same gas. The fraction of star formation that ends up in clusters is apparently not constant, but may increase with interstellar pressure

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Lusia, Dwi Ayu, Imelda Salsabila, Heni Kusdarwati, and Suci Astutik. "CLUSTERING DISTRICTS/CITIES IN EAST JAVA PROVINCE BASED ON HIV CASES USING K-MEANS, AGNES, AND ENSEMBLE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 1 (2025): 63–72. https://doi.org/10.30598/barekengvol19iss1pp63-72.

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Cluster analysis is a method of grouping data into certain groups based on similar characteristics. This research aims to group districts/cities in East Java Province in 2021 based on HIV cases using hierarchical cluster analysis (AGNES), non-hierarchical cluster analysis (K-means), and ensemble clustering. The study found that the ensemble clustering solution forms four clusters, consistent with the results of AGNES clustering. This suggests that ensemble clustering improves the quality of cluster solutions by leveraging both hierarchical and non-hierarchical methods. The grouping of district

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Sitikantha, Sitikantha, Suneeta Mohanty, and Bhabani Shankar Prasad Mishra. "Neutrosophic Hierarchical Clustering: A Novel Approach for Handling Uncertainty in Multi-Level Data Organization." International Journal of Neutrosophic Science 26, no. 1 (2025): 243–53. https://doi.org/10.54216/ijns.260121.

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The most important stage of data mining is clustering. Several distinct clustering approaches like grid-based, density-based, partitioning, graph-based, model-based, and hierarchical clustering are used for cluster analysis. We can cluster data objects into hierarchical trees by using the hierarchical clustering approach. Hierarchical clustering, with its agglomerative and divisive types, uses nodes to represent clusters. Agglomerative clustering is favored, and high-quality clusters are essential for successful cluster analysis. Up to this point, numerous alternatives to the clustering techni

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Muhamed, Lekaa, and Hayder Mohammed. "On Clustering Scheme for Kernel K-Means." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 1, 2021): 544–54. http://dx.doi.org/10.55562/jrucs.v46i1.106.

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Cluster analysis mainly concerned with dividing the number of data elements into clusters observation in the same cluster are homogeneous and are not homogeneous with other clusters, but in the case of nonparametric data it is not possible to deal with classic estimated because of obtaining misleading results This gave rise to adopt efficient estimation methods known as the kernel methods. One of the methods of clustering is Non-Hierarchical clustering aims to divide the dataset into (k) homogeneous cluster groups based on the idea of the central the tendency of the cluster group using (k) ave

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Utari, Dina Tri, and Denesa Salma Hanun. "Hierarchical Clustering Approach for Region Analysis of Contraceptive Users." EKSAKTA: Journal of Sciences and Data Analysis 2, no. 2 (2021): 99–108. http://dx.doi.org/10.20885/eksakta.vol2.iss1.art12.

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Through increasing the use of contraceptives to limit births, the Family Planning (KB) Program is one of the government's efforts to control the rate of population growth. Klaten Districts is one of the regencies in Central Java Province with a relatively high number of births and relatively low coverage of active family planning. This study aimed to determine the grouping of sub-districts and these characteristics in the Klaten Districts in 2020. The method used in this study was a hierarchical cluster analysis method, with the best method being the centroid method. In this study obtained 3 c

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Fahrudin, Tresna Maulana, Prismahardi Aji Riyantoko, Kartika Maulida Hindrayani, and Made Hanindia Prami Swari. "Cluster Analysis of Hospital Inpatient Service Efficiency Based on BOR, BTO, TOI, AvLOS Indicators using Agglomerative Hierarchical Clustering." Telematika 18, no. 2 (2021): 194. http://dx.doi.org/10.31315/telematika.v18i2.4786.

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Purpose: The research proposed an approach for grouping hospital inpatient service efficiency that have the same characteristics into certain clusters based on BOR, BTO, TOI, and AvLOS indicators using Agglomerative Hierarchical Clustering.Design/methodology/approach: Applying Agglomerative Hierarchical Clustering with dissimilarity measures such as single linkage, complete linkage, average linkage, and ward linkage.Findings/result: The experiment result has shown that ward linkage was given a quite good score of silhouette coefficient reached 0.4454 for the evaluation of cluster quality. The

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Jan, Bilal, Haleem Farman, Huma Javed, Bartolomeo Montrucchio, Murad Khan, and Shaukat Ali. "Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey." Wireless Communications and Mobile Computing 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/6457942.

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Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are genera

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Suraya, Ghina Rofifa, and Arie Wahyu Wijayanto. "Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting." Indonesian Journal of Statistics and Its Applications 6, no. 2 (2022): 180–201. http://dx.doi.org/10.29244/ijsa.v6i2p180-201.

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Stunting has been widely known as the highest case of malnutrition suffered by toddlers in the world and has a bad impact on children's future. In 2018, Indonesia was ranked the 31st highest stunting in the world and ranked 4th in Southeast Asia. About 30.8% (roughly 3 out of 10) of children under 5 years suffer from stunting in Indonesia. To support the government policy making in handling stunting, it is undoubtedly necessary to classify the levels of stunting handling in regions in Indonesia. In this work, the hierarchical agglomerative and non-hierarchical clustering is compared and evalua

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Puspita, Ika, Memi Nor Hayati, and Darnah Andi Nohe. "Pengelompokan Puskesmas Berdasarkan Kasus Balita Stunting di Kabupaten Paser Menggunakan Metode K-Medoids." EKSPONENSIAL 14, no. 1 (2023): 1. http://dx.doi.org/10.30872/eksponensial.v14i1.1089.

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The number of cases of stunting toddler in Paser Regency increased by 6.66% from 2018 to 2019%. The increased in the number of stunting toddler in Paser Regency shows that the efforts made by the Paser Regency Government have not been effective in reducing the prevalence of stunting toddler because the stunting toddler rate in Paser Regency is still above the threshold set by the World Health Organization (WHO), which is a maximum of 20%. Therefore, an appropriate strategy is needed to find out which areas receive special attention and treatment, one of method to be used is cluster analysis. C

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Agada, I. O., O. Peter, and E. J. Eweh. "Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria." Nigerian Journal of Theoretical and Environmental Physics 2, no. 1 (2024): 36–48. http://dx.doi.org/10.62292/njptep.v2i1.2024.19.

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Precipitation is highly unpredictable over space and time, and is an indicator for climate change. The present work classified average yearly precipitation over the 36 states and the Federal Capital Territory (FCT) in Nigeria into various groups. The hierarchical and non- hierarchical (k-means) cluster method was used in the classification of 37- years precipitation data (1984-2020). The results showed that six major clusters were formed. The Precipitation across the 36 states and the FCT in Nigeria can be classified as Light precipitation for Sokoto, Borno, Kastina, Yobe and Kano States; Mode

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Talekar, S. C., M. Vani Praveena, and R. G. Satish. "Genetic diversity using principal component analysis and hierarchical cluster analysis in rice." INTERNATIONAL JOURNAL OF PLANT SCIENCES 17, no. 2 (2022): 191–96. http://dx.doi.org/10.15740/has/ijps/17.2/191-196.

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A set of 100 germplasm lines with four checks viz., BPT-5204, PSB-68, Siri1253 and MGD-101 were evaluated in augmented block design during Kharif 2020. The observations were documented for 5 quantitative traits viz., days to 50% flowering, panicle length, number of panicles per square meter, 1000 grain weight and grain yield by principal component analysis and cluster analysis to determine the relationship and genetic divergence among the individuals. The cumulative variance of 55.60% was explained by 1st two principal components (PC1 and PC2) with eigen values greater than 1. Component 1 with

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Widyawati, Widyawati, Wawan Laksito Yuly Saptomo, and Yustina Retno Wahyu Utami. "Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan." Jurnal Ilmiah SINUS 18, no. 1 (2020): 75. http://dx.doi.org/10.30646/sinus.v18i1.448.

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As more businesses emerge, companies need to have the right marketing strategy to provide the best service to customers. The first step is to know the type of customer and make appropriate marketing strategies according to the type of customer. In this research, it is proposed for clustering customers so that an appropriate strategy for that customer group can be determined. The method used for cluster formation uses Agglomerative Hierarchical Clustering with Average Linkage approach and distance determination using Manhattan Distance. The variables in this research are Recency, Frequency, and

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Fitriyah, Any Tsalasatul. "PENERAPAN METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING UNTUK KLASIFIKASI PROGRAM STUDI BERDASARKAN KUALITAS PELAYANAN MAHASISWA." IQTISHADUNA 8, no. 2 (2017): 194–202. https://doi.org/10.20414/iqtishaduna.v8i2.394.

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Penelitian ini bertujuan untuk mengetahui penerapan metodeagglomerative hierarchical clustering untuk klasifikasi program studiberdasarkan pelayanan terhadap mahasiswa dan mengetahui klasifikasiprogram studi berdasarkan pelayanan terhadap mahasiswa berdasarkanmetode agglomerative hierarchical clustering. Penelitian ini dilakukan diInstitut Agama Islam Negeri (IAIN) Mataram. Objek penelitian iniadalah mahasiswa aktif semester dua, empat, enam, dan delapan daridua belas program studi yang ada di IAIN Mataram. Data diperolehdengan menyebarkan kuesioner kepada 442 responden. Terdapat limaaspek yan

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Warsito, B., S. Sumiyati, H. Yasin, and H. Faridah. "Evaluation of river water quality by using hierarchical clustering analysis." IOP Conference Series: Earth and Environmental Science 896, no. 1 (2021): 012072. http://dx.doi.org/10.1088/1755-1315/896/1/012072.

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Abstract Assessment of water pollution is a critical study because it can affect humans directly. Likewise, river water is widely used for various daily needs. It is important to group rivers according to their classes so that further analysis and action can be carried out. This article discusses the clustering of rivers in several areas in the southeast part of Central Java Province consisting of 14 sampling stations based on several water quality parameters. The pollutant parameters include TSS, electrical conductivity, pH, BOD, COD, and DO. The method used is Hierarchical clustering in whic

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Malathi, L., and R. K. Gnanamurthy. "Cluster Based Hierarchical Routing Protocol for WSN with Energy Efficiency." International Journal of Machine Learning and Computing 4, no. 5 (2014): 474–77. http://dx.doi.org/10.7763/ijmlc.2014.v4.457.

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Eric U., Oti,, and Olusola, Michael O. "Overview of Agglomerative Hierarchical Clustering Methods." British Journal of Computer, Networking and Information Technology 7, no. 2 (2024): 14–23. http://dx.doi.org/10.52589/bjcnit-cv9poogw.

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Agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods uses a bottom-up approach and it starts clustering by treating the individual data points as a single cluster, then it is merged continuously based on similarity until it forms one big cluster containing all objects. In this paper, we reviewed eight agglomerative hierarchical clustering methods namely: single linkage method, complete linkage method, average linkage method, weighted group average method, centroid method, medi

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Hu, Minhui, Kaiwei Zeng, Yaohua Wang, and Yang Guo. "Threshold-Based Hierarchical Clustering for Person Re-Identification." Entropy 23, no. 5 (2021): 522. http://dx.doi.org/10.3390/e23050522.

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Unsupervised domain adaptation is a challenging task in person re-identification (re-ID). Recently, cluster-based methods achieve good performance; clustering and training are two important phases in these methods. For clustering, one major issue of existing methods is that they do not fully exploit the information in outliers by either discarding outliers in clusters or simply merging outliers. For training, existing methods only use source features for pretraining and target features for fine-tuning and do not make full use of all valuable information in source datasets and target datasets.

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Arif, Besya Salsabilla Azani, Agus Rusgiyono, and Abdul Hoyyi. "PENGELOMPOKAN PROVINSI-PROVINSI DI INDONESIA MENGGUNAKAN METODE WARD (StudiKasus: Produksi Tanaman Pangan di Indonesia Tahun 2018)." Jurnal Gaussian 9, no. 1 (2020): 112–21. http://dx.doi.org/10.14710/j.gauss.v9i1.27528.

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Cluster analysis is a technique for grouping objects or observations into homogeneous groups. Cluster analysis is divided into two methods, namely hierarchy and non-hierarchy. The hierarchy method generally involves a series of n-1 decisions (n is the number of observations) that combine observations into a tree-like structure or dendogram. Hierarchy is divided into two methods, namely agglomerative (concentration) and splitting (distribution). For non-hierarchical methods, the number of clusters can be determined by the researcher. Ward method is a hierarchical cluster analysis method that ca

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Kang, Mun-Su, and Young-Sik Choi. "Ant Colony Hierarchical Cluster Analysis." Journal of Internet Computing and Services 15, no. 5 (2014): 95–105. http://dx.doi.org/10.7472/jksii.2014.15.5.95.

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Nurfajriyani Nurfajriyani, Dentina Dewi Amaliana, and Sri Pingit Wulandari. "Pengelompokan Provinsi berdasarkan Aspek Pembangunan Pendidikan di Indonesia Tahun 2023 menggunakan Analisis Cluster." Pentagon : Jurnal Matematika dan Ilmu Pengetahuan Alam 2, no. 4 (2024): 134–53. https://doi.org/10.62383/pentagon.v2i4.309.

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Improving the quality of Human Resources (HR) is a major challenge in facing global competition. Education as the main means of improving the quality of HR in Indonesia is still faced with the problem of inequality of access and quality between regions. This inequality causes disparities in educational development between urban and remote areas. This study focuses on grouping provinces in Indonesia based on aspects of educational development in 2023, using cluster analysis. Secondary data from the Central Statistics Agency (BPS) is used as the basis for analysis, including variables of average

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Narita, Kakeru, Teruhisa Hochin, Yoshihiro Hayashi, and Hiroki Nomiya. "Incremental Hierarchical Clustering for Data Insertion and Its Evaluation." International Journal of Software Innovation 8, no. 2 (2020): 1–22. http://dx.doi.org/10.4018/ijsi.2020040101.

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Clustering is employed in various fields. However, the conventional method does not consider changing data. Therefore, if the data is changed, the entire dataset must be re-clustered. This article proposes a clustering method to update the clustering result obtained by a hierarchical clustering method without re-clustering when a point is inserted. This article defines the center and the radius of a cluster and determine the cluster to be inserted. The insertion location is determined by similarity based on the conventional clustering method. this research introduces the concept of outliers an

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Octavanny, Made Ayu Dwi. "Analisis pengelompokan jumlah tanaman kehutanan yang diusahakan menurut jenis tanaman di Indonesia." Majalah Ilmiah Matematika dan Statistika 24, no. 1 (2024): 27. http://dx.doi.org/10.19184/mims.v24i1.39575.

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One of the problems of archipelagic countries is the lack of maximum utilization of natural resources which has resulted in some areas being left behind. Indonesia is one of those who experience the impact of the lack of utilization of natural resources in the forestry sector. The non-optimal use of forests for planting forestry plants has made most Indonesians use their land as artificial forests, namely to plant forestry plants. Cluster analysis in this case seeks to classify provinces in Indonesia based on the type of forestry plants cultivated. The method used is hierarchical and non-hiera

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T. Ezhilarasi, A. Mahalingam, and N. Manivannan. "Assessment of genetic diversity in Indian sesame (Sesamum indicum L.) germplasm." Ecology, Environment and Conservation 30, SUPPL (2024): S344—S348. http://dx.doi.org/10.53550/eec.2024.v30i03s.059.

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Principal component analysis and hierarchical clustering were carried out for 151 Indian sesame germplasm for five quantitative traits, viz., days to fifty percent flowering, days to maturity, plant height (cm), number of capsules per plant and seed yield per plant (g). Among five principal components, PC I and PC II alone recorded above 1 eigenvalue. The first two principal components accounted for 62.45% of cumulative total variations. PC I contributed the maximum variability of 40%, followed by PC II (21.68%) of the total variation. Days to fifty percent flowering and days to maturity had h

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Suprihartiningsih, Erna, and Sri Juli Asdiyanti Samuda. "Metode Cluster Hirarki pada Data Margin Perdagangan dan Pengangkutan Komoditas Strategis di Indonesia Tahun 2021." Ecoplan 6, no. 1 (2023): 8–20. http://dx.doi.org/10.20527/ecoplan.v6i1.633.

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This study aims to compare the hierarchical cluster analysis method to classify the value of trading and transportation margin (MPP) into three groups, each with specific characteristics for each commodity strategy. Cophenetic correlation analysis shows that the average hierarchical cluster method is the best for classifying strategic commodity MPPs in Indonesia in 2021. Cluster 1 has moderate characteristics (MPP) for shallots, rice, and purebred chicken. At the same time, Red Chili has low MPP characteristics in this cluster. Hopefully, this study can help policymakers make strategic decisio

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Frontera, Jennifer A., Lorna E. Thorpe, Naomi M. Simon, et al. "Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study." PLOS ONE 17, no. 9 (2022): e0275274. http://dx.doi.org/10.1371/journal.pone.0275274.

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Background Post-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management. Methods In a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 inf

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Schonlau, Matthias. "Visualizing non-hierarchical and hierarchical cluster analyses with clustergrams." Computational Statistics 19, no. 1 (2004): 95–111. http://dx.doi.org/10.1007/bf02915278.

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Enseih, Davoodi Jam, Nematbakhsh Mohammadali, and Askarizade Mojgan. "BRAIN Journal - Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 3, no. 3 (2012): 62–70. https://doi.org/10.5281/zenodo.1043297.

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ABSTRACT Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on numerical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are three basic groups of clustering methods: partitional methods, hierarchical methods and densitybased methods. This paper proposes a hybrid clustering algorithm that combines the advantages of hierarchical clustering and fuzzy clustering techniques and considers mixed attributes. The proposed algorithms i

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Fadliana, Alfi, and Fachrur Rozi. "Penerapan Metode Angglomerative Hierarchical Clustering untuk Klasifikasi Kabupaten/Kota di Propinsi Jawa Timur Berdasarkan Kualitas Pelayanan Keluarga Berencana." CAUCHY 4, no. 1 (2015): 25. http://dx.doi.org/10.18860/ca.v4i1.3172.

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Agglomerative hierarchical clustering methods is cluster analysis method whose primary purpose is to group objects based on its characteristics, it begins with the individual objects until the objects are fused into a single cluster. Agglomerative hierarchical clustering methods are divided into single linkage, complete linkage, average linkage, and ward. This research compared the four agglomerative hierarchical clustering methods in order to get the best cluster solution in the case of the classification of regencies/cities in East Java province based on the quality of “Keluarga Berencana” (

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WANG, LIN, MINGHU JIANG, YINGHUA LU, MINFU SUN, and FRANK NOE. "A COMPARATIVE STUDY OF CLUSTERING METHODS FOR MOLECULAR DATA." International Journal of Neural Systems 17, no. 06 (2007): 447–58. http://dx.doi.org/10.1142/s0129065707001287.

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The research aim is to use three clustering technologies for establishing molecular data model of large size sets by comparison between low energy samples (LES) and local molecular samples (LMS). Hierarchical cluster of multi-level tree distance relation, competitive learning network of similar inputs falling into the same cluster and topological SOM are used to analyze 6242 LES and 5000 LMS. Our experiments show that in SOM, there are 24 to 25 Davies-Boulding clustering index and color map cluster units in the LES more than 10 to 12 in the LMS, which is consistent with the results of hierarch

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Liu, Lanfa, Song Wang, Zichen Tong, and Zhanchuan Cai. "Clustering-Based Class Hierarchy Modeling for Semantic Segmentation Using Remotely Sensed Imagery." Mathematics 13, no. 3 (2025): 331. https://doi.org/10.3390/math13030331.

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Land use/land cover (LULC) nomenclature is commonly organized as a tree-like hierarchy, contributing to hierarchical LULC mapping. The hierarchical structure is typically defined by considering natural characteristics or human activities, which may not optimally align with the discriminative features and class relationships present in remotely sensed imagery. This paper explores a novel cluster-based class hierarchy modeling framework that generates data-driven hierarchical structures for LULC semantic segmentation. First, we perform spectral clustering on confusion matrices generated by a fla

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Silva, Richard, Thaisa Azevedo, Hiago Martins, and Lucas Cardoso Pereira. "Cluster analysis applied to fuel price data in Campina Grande." Socioeconomic Analytics 2, no. 1 (2024): 137–43. https://doi.org/10.51359/2965-4661.2024.265075.

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This work uses cluster analysis to analyze similarities in the prices of regular gasoline and ethanol in Campina Grande-PB, Brazil. This multivariate statistical technique groups similar elements into distinct clusters using the non-hierarchical method (k-means). The analysis reports three clusters: cluster 1 has the lowest prices, while cluster 3 has the highest. We report descriptive statistics and a discussion based on the empirical local context.

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