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Academic literature on the topic 'Davies Bouldin Index'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Davies Bouldin Index"

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Ren, Shiyuan. "Integration and Optimization of Educational Teaching Resources in Colleges and Universities Based on Clustering Algorithm." Journal of Electrical Systems 20, no. 6s (2024): 2156–65. http://dx.doi.org/10.52783/jes.3130.

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The paper presents Centroid Ranking Optimized Clustering (CROC), a novel approach aimed at enhancing educational teaching and resource optimization through advanced clustering techniques. CROC integrates centroid-based clustering with ranking optimization strategies to segment students or educational resources into meaningful clusters, facilitating personalized learning experiences and targeted interventions. Experimental analysis conducted on real-world educational datasets demonstrates that CROC outperforms traditional clustering algorithms in terms of clustering quality, accuracy, and inter
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Dayat, Moh Nurdayat, Nana Suarna, and Yudhistira Arie Wijaya. "Analisa Clustering untuk Mengelompokan Data Penayangan Film Bioskop Menggunakan Algoritma K-Means." INTERNAL (Information System Journal) 6, no. 1 (2023): 68–78. http://dx.doi.org/10.32627/internal.v6i1.686.

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The purpose of this study is one of the analyzes to obtain film screening data, the approach used in this study is the K-means algorithm using the parameter measure type Numerical Measure with Numerical Measure Euclidean Distance to get the best Davies Bouldin Index (DBI), with the intention of getting helps grouping datasets of film screenings at the Ramayana Cirebon XXI Cinema. Results from the evaluation of the Davies Bouldin Index (DBI) obtained is (K-2) with a Davies Bouldin Index (DBI) value of 0.864, because the value obtained is the smaller the Davies Bouldin Index (DBI) value, it show
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Pangestu, Mohamad Sugeng, and Maulida Ayu Fitriani. "Perbandingan Perhitungan Jarak Euclidean Distance, Manhattan Distance, dan Cosine Similarity dalam Pengelompokan Data Bibit Padi Menggunakan Algoritma K-Means." Sainteks 19, no. 2 (2022): 141. http://dx.doi.org/10.30595/sainteks.v19i2.14495.

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Bibit padi yang mempunyai kualitas unggul memiliki peran penting dalam peningkatan produktivitas pada sektor pertanian. Banyaknya bibit padi yang dikembangkan oleh Balai Besar Penelitian Tanaman Padi menghasilkan karakteristik bibit padi baru serta mempunyai kemiripan karakteristik yang hampir sama. Bibit padi yang memiliki kemiripan berdasarkan karakteristiknya dapat dikelompokkan dengan menggunakan metode Clustering dimana dalam proses perhitungannya menggunakan metode pengukuran jarak. Pada penelitian ini menggunakan algoritma K-Means dengan metode Euclidean Distance, Manhattan Distance, da
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Butsianto, Sufajar, and Nurhali Saepudin. "Penerapan Data Mining Terhadap Minat Siswa Dalam Mata Pelajaran Matematika Dengan Metode K-Means." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 3, no. 1 (2020): 51–59. http://dx.doi.org/10.32672/jnkti.v3i1.2008.

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Kumpulan data nilai siswa pada sebuah sekolah dapat diolah dengan memanfaatkan teknologi data mining untuk menghasilkan pengetahuan menarik dan bermanfaat, yang selama ini tidak diketahui secara manual. Salah satu teknik data mining adalah clustering. Algoritma K-Means dapat digunakan untuk mengelompokan minat siswa terhadap mata pelajaran matematika pada sebuah sekolah sehingga dapat bermanfaat bagi pengguna kebijakan dalam proses pengambilan keputusan. Proses ini menghasilkan 2 cluster yaitu ( minat ) Matematika dan ( kurang minat ) matematika, dengan menggunakan teknik data mining menggunak
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Candrasari, Diwahana Mutiara. "Pemilihan Pelanggan Potensial Dengan Melakukan Pemetaan Area Dengan Metode Algoritma K-NN dan K-Means Di Yamaha Nusantara Motor Purwokerto." Joined Journal (Journal of Informatics Education) 4, no. 2 (2021): 52. http://dx.doi.org/10.31331/joined.v4i2.1942.

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This study proposes a clustering of potential customers at Yamaha Nusantra Motor Purwokerto based on consumer characteristics. Clustering is one of the processes of data mining that aims to partition existing objects into one or more clusters of objects based on their characteristics. The method used in this study is K-Nearest Neighbor as a determination of the feasibility of the data while K-Means is used as customer clustering. The value of the Davies Bouldin Index was examined using rapidminer while the purity validation was using Microsoft excel. The results show that the K-Means training
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Hossen, Md Amran, Biswanath Bhattacharjee, Sonjoy Kumar Dey, et al. "BUSINESS ANALYTICS FOR CUSTOMER SEGMENTATION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS IN PERSONALIZED BANKING SERVICES." International Journal of Economics Finance & Management Science 10, no. 03 (2025): 1–13. https://doi.org/10.55640/ijefms/volume10issue03-01.

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This study evaluates three machine learning clustering algorithms—K-Means, DBSCAN, and Hierarchical Clustering—for customer segmentation in the banking sector. Using a dataset of customer demographic, financial, and transactional data, we compare the algorithms based on the Silhouette score and Davies-Bouldin index. Hierarchical Clustering performed best, achieving the highest Silhouette score (0.68) and the lowest Davies-Bouldin index (1.15), indicating well-defined and compact clusters. K-Means showed reliable performance with a Silhouette score of 0.62 but required predefined clusters. DBSC
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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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Eliza, Novi, Ismail Husein, and Sajaratud Dur. "Determining Zoning of Areas Affected by Flood Disasters in Medan City Using Silhouette Coefficient and Davies Bouldin Index Analysis." Jurnal Pijar Mipa 19, no. 3 (2024): 558–63. http://dx.doi.org/10.29303/jpm.v19i3.6707.

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Several sub-districts are cities that are pretty or even very vulnerable to flood disasters. Therefore, the Government must observe which areas are highly prone to flooding to anticipate safety precautions. One of the relevant methods for handling this case is k-means clustering using the Silhouette Coefficient and Davies-Bouldin Index evaluation. This research uses a quantitative approach using the Silhouette Coefficient and Davies Bouldin Index Analysis method. Based on the research that has been carried out, the results can be obtained that cluster 1 consists of the sub-districts of Medan P
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Orisa, Mira. "Optimasi Cluster pada Algoritma K-Means." Prosiding SENIATI 6, no. 2 (2022): 430–37. http://dx.doi.org/10.36040/seniati.v6i2.5034.

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Metode evaluasi yang digunakan adalah metode-metode internal. Metode internal melakukan evaluasi dengan melihat seberapa jauh jarak antar cluster dan seberapa padat cluster-cluster tersebut. Pengklasterisasian data dimodelkan menggunakan algoritma K-Means. Algoritma K-Means memiliki kelemahan dalam menentukan centroid awal. Centroid awal ditentukan secara random/acak untuk sejumlah k cluster yang dipilih. Sehingga keluaran yang dihasilkan bergantung pada pemilihan centroid awal tersebut. Algoritma K-Means harus dijalankan berulang kali untuk mendapatkan hasil cluster yang optimal. Evaluasi clu
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Bagustio, Agung Prayogo, Ade Irma Purnamasari, and Irfan Ali. "ANALISIS DATA PENJUALAN MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING PADA TOKO KECANTIKAN PUTRI." PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer 11, no. 2 (2024): 159–67. http://dx.doi.org/10.30656/prosisko.v11i2.7928.

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Abstrak - Industri kecantikan mengalami pertumbuhan pesat dalam beberapa tahun terakhir, mendorong tingginya permintaan produk kecantikan. Toko Kecantikan Putri merupakan salah satu toko kecantikan yang berkembang pesat di wilayah Cirebon. Untuk meningkatkan strategi penjualan dan memahami pola pembelian pelanggan, Toko Kecantikan Putri perlu menganalisis data penjualan secara efektif. Analisis data penjualan tradisional tidak dapat memberikan insights yang mendalam mengenai pola pembelian pelanggan. Hal ini dapat menghambat Toko Kecantikan Putri dalam mengembangkan strategi penjualan yang tep
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Book chapters on the topic "Davies Bouldin Index"

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Choo, Hyunwoo, Heejung Hyun, and Sungjun Hong. "Clustering Voice of the Customer Insights: Identifying Key Needs for AI-Based Early Warning System." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250416.

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In this study, we analyzed voice of customer (VOC) data for an AI-based early warning system from healthcare providers using the BERTopic framework for effective topic modeling. A preprocessing pipeline was implemented, incorporating techniques such as lowercasing, stopword removal, and tokenization to prepare the text for analysis. To refine the model’s performance, hyperparameter optimization was conducted using Optuna, with a primary focus on maximizing the Silhouette Score and minimizing the Davies-Bouldin Index, while also assessing the diversity of the generated topics. Ultimately, we id
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Zakrzewska, Danuta. "Validation of Clustering Techniques for Student Grouping in Intelligent E-learning Systems." In Knowledge-Based Intelligent System Advancements. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-811-7.ch012.

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An intelligent e-learning system should be enhanced with personalization features that enable it to be tailored to different students’ needs. The individual requirements of learners may depend on their characteristic traits, such as dominant learning styles. Finding groups of students with similar preferences can help when systems are being adjusted for individual requirements. The performance of personalized educational systems is dependant upon the number and quality of student clusters obtained. In this chapter the application of clustering techniques for grouping students according to thei
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Bhimavarapu, Usharani. "Legal and Psychological Resilience." In Psychological Evaluations in Immigration Cases. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7944-8.ch009.

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Legal and psychological factors play a crucial role in shaping the experiences of immigrants, influencing their integration and well-being in a new country. This study explores the relationship between these factors through clustering and validation techniques applied to a diverse set of demographic, psychological, and employment data collected from immigrant populations. Various clustering models, including K-Means, Hierarchical Clustering, and DBSCAN, were used to categorize participants based on resilience, stress, and other demographic characteristics. Performance of these models was asses
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Veloso, Rui, Filipe Portela, Manuel Filipe Santos, et al. "Categorize Readmitted Patients in Intensive Medicine by Means of Clustering Data Mining." In Hospital Management and Emergency Medicine. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2451-0.ch005.

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With a constant increasing in the health expenses and the aggravation of the global economic situation, managing costs and resources in healthcare is nowadays an essential point in the management of hospitals. The goal of this work is to apply clustering techniques to data collected in real-time about readmitted patients in Intensive Care Units in order to know some possible features that affect readmissions in this area. By knowing the common characteristics of readmitted patients it will be possible helping to improve patient outcome, reduce costs and prevent future readmissions. In this stu
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Silva Lincoln F., Sequeiros Giomar O., Santos Maria Lúcia O., Fontes Cristina A.P., Muchaluat-Saadea Débora C., and Conci Aura. "Thermal Signal Analysis for Breast Cancer Risk Verification." In Studies in Health Technology and Informatics. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-564-7-746.

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Breast cancer is the second most common cancer in the world. Currently, there are no effective methods to prevent this disease. However, early diagnosis increases chances of remission. Breast thermography is an option to be considered in screening strategies. This paper proposes a new dynamic breast thermography analysis technique in order to identify patients at risk for breast cancer. Thermal signals from patients of the Antonio Pedro University Hospital (HUAP), available at the Mastology Database for Research with Infrared Image - DMR-IR were used to validate the study. First, each patient'
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Hamou, Reda Mohamed, Abdelmalek Amine, Mohamed Amine Boudia, and Ahmed Chaouki Lokbani. "An Optimal Configuration of Sensitive Parameters of PSO Applied to Textual Clustering." In Exploring Critical Approaches of Evolutionary Computation. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5832-3.ch010.

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The clustering aims to minimize intra-class distance in the cluster and maximize extra-classes distances between clusters. The text clustering is a very hard task; it is solved generally by metaheuristic. The current literature offers two major metaheuristic approaches: neighborhood metaheuristics and population metaheuristics. In this chapter, the authors seek to find the optimal configuration of sensitive parameters of the PSO algorithm applied to textual clustering. The study will go through in dissociable steps, namely the representation and indexing textual documents, clustering by biomim
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Hidayat, Syahroni, Budi Sunarko, and Uswatun Hasanah. "K-Means Clustering for Profiling Logical-Mathematical Intelligence and Problem-Solving Abilities." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9846-3.ch007.

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Intelligence is the ability to think, experience learning, problem-solving, and adaptation to new situations. Thus, intelligence is an essential foundation in the learning process. Logical-mathematical intelligence (KLM) is a strong indicator for assessing individual intelligence levels and learning achievements. KLM significantly correlates to the ability to solve story problems (KSC). However, exploring these two variables to group students to help educators prepare good learning strategies has not yet been done. Therefore, this study applied the K-means algorithm to reveal it all by utilizi
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Venkatesan, Anusuya S. "Optimized Clustering Techniques with Special Focus to Biomedical Datasets." In Biomedical Engineering. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3158-6.ch049.

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The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in decision making process which in turn helps for the diagnosis and treatment of the diseases. Biomedical datasets are a collection of data with diverse types as it involves images, clinical studies, stat
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Venkatesan, Anusuya S. "Optimized Clustering Techniques with Special Focus to Biomedical Datasets." In Computational Tools and Techniques for Biomedical Signal Processing. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0660-7.ch015.

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The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in decision making process which in turn helps for the diagnosis and treatment of the diseases. Biomedical datasets are a collection of data with diverse types as it involves images, clinical studies, stat
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Conference papers on the topic "Davies Bouldin Index"

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Henderi, H., Liza Fitriana, I. Iskandar, et al. "Optimization of Davies-Bouldin Index with k-medoids algorithm." In SCIENCE AND TECHNOLOGY RESEARCH SYMPOSIUM 2022. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0225220.

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Rojas Thomas, J. C., M. Mora Cofre, and M. Santos. "New Version of Davies-Bouldin index for clustering validation based on hyper rectangles." In 6th Chilean Conference on Pattern Recognition (CCPR). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/14.2014.0001.

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Rojas Thomas, Juan Carlos, Matilde Santos Penas, and Marco Mora. "New Version of Davies-Bouldin Index for Clustering Validation Based on Cylindrical Distance." In 2013 32nd International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2013. http://dx.doi.org/10.1109/sccc.2013.29.

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Singh, Akhilesh Kumar, Shantanu Mittal, Prashant Malhotra, and Yash Vardhan Srivastava. "Clustering Evaluation by Davies-Bouldin Index(DBI) in Cereal data using K-Means." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020. http://dx.doi.org/10.1109/iccmc48092.2020.iccmc-00057.

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Karo, Ichwanul Muslim Karo, Kiki MaulanaAdhinugraha, and Arief Fatchul Huda. "A cluster validity for spatial clustering based on davies bouldin index and Polygon Dissimilarity function." In 2017 Second International Conference on Informatics and Computing (ICIC). IEEE, 2017. http://dx.doi.org/10.1109/iac.2017.8280572.

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Coelho, Guilherme P., Celso C. Barbante, Levy Boccato, Romis R. F. Attux, Jose R. Oliveira, and Fernando J. Von Zuben. "Automatic feature selection for BCI: An analysis using the davies-bouldin index and extreme learning machines." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252500.

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Tempola, Firman, and Achmad Fuad Assagaf. "Clustering of Potency of Shrimp In Indonesia With K-Means Algorithm And Validation of Davies-Bouldin Index." In Proceedings of the International Conference on Science and Technology (ICST 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/icst-18.2018.148.

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Muningsih, Elly, Chandra Kesuma, Sunanto, Suripah, and Aprih Widayanto. "Combination of K-Means method with Davies Bouldin index and decision tree method with parameter optimization for best performance." In 2ND INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENTIFIC DEVELOPMENT (ICAISD) 2021: Innovating Scientific Learning for Deep Communication. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0129119.

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Fahmi, Muhammad Farid, Yoyon K. Suprapto, and Wirawan. "Segmentation and distribution of watershed using K-Modes clustering algorithm and Davies-Bouldin index based on geographic information system (GIS)." In 2016 International Seminar on Application for Technology of Information and Communication (ISemantic). IEEE, 2016. http://dx.doi.org/10.1109/isemantic.2016.7873844.

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Prajapati, Miit, Vaibhav Payghan, Abhisha Chauhan, and Kranthi Nidubrolu. "Comparative Study of Unsupervised Clustering Methods Used for RADAR Applications." In Symposium on International Automotive Technology. SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0029.

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<div class="section abstract"><div class="htmlview paragraph">Driver safety has become an important aspect. To have driver safety RADAR is an essential part of vehicles hence RADAR has great significance in the automotive industry. The Radar sensor collects data from surroundings that may have unwanted data that may lead to improper detections of intended objects, so to have proper object detections it is needed to use clustering methods on the radar point cloud data. There are numerous unsupervised clustering methods used for RADAR applications. In this paper, the comparisons of d
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