Gotowe bibliografie tematyczne / Calinski-Harabasz

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Gotowa bibliografia na temat „Calinski-Harabasz”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Calinski-Harabasz"

Caglar, Cengizler, and Kerem Un M. "Evaluation of Calinski-Harabasz Criterion as Fitness Measure for Genetic Algorithm Based Segmentation of Cervical Cell Nuclei." British Journal of Mathematics & Computer Science 22, no. 6 (2017): 1–13. https://doi.org/10.9734/BJMCS/2017/33729.

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In this paper, the classication capability of Calinski-Harabasz criterion as an internal cluster validation measure has been evaluated for clustering-based region discrimination on cervical cells. In this approach, subregions in the sample image are initially randomly constructed to be the individuals of the population. At each generation, individuals are evaluated according to their Accordingly a novel genetic structure for meta heuristic area isolation is proposed. Evaluation of proposed combination of genetic algorithm and Calinski-Harabasz measure is achieved by experiments, conducted on r

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Zhang, Wenbo, Zixian Yue, Jinmei Ye, et al. "Modulation format identification using the Calinski–Harabasz index." Applied Optics 61, no. 3 (2022): 851. http://dx.doi.org/10.1364/ao.448043.

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He, Zhihao, Weiduo Qin, and Changping Duan. "Chemical composition analysis of ancient glass products based on decision tree." Highlights in Science, Engineering and Technology 42 (April 7, 2023): 211–19. http://dx.doi.org/10.54097/hset.v42i.7097.

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Due to the effects of prolonged burial, freshly unearthed ancient glass is often weathered to varying degrees, and it is difficult to identify the type of glass. We introduce machine learning into the composition analysis and type identification of ancient glass products. This objective is to build a reliable ancient glass classification model based on decision trees and two different k-means clustering algorithms. The performance of the decision tree is measured by the ROC curve. The performance of its clustering algorithm was evaluated by the Calinski-Harabasz index. The results show that th

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Daniel, Ikechukwu, Lateef Akinyemi, and Obianuju Udekwu. "Identifying Landslide Hotspots Using Unsupervised Clustering: A Case Study." Journal of Future Artificial Intelligence and Technologies 1, no. 3 (2024): 249–68. http://dx.doi.org/10.62411/faith.3048-3719-37.

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Landslides pose significant threats to life, property, and infrastructure. This study explores applying unsupervised learning techniques to identify and understand landslide-prone areas. We analyzed topographic data by employing K-Means, Hierarchical Clustering, Spectral Clustering, Mean Shift Clustering, and DBSCAN to uncover hidden patterns in landslide occurrence. Evaluation metrics, including the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to assess the performance of these algorithms. Hierarchical Clustering achieved the highest Silhouette Score of 0.635

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Rianti, Resa, Roni Andarsyah, and Rolly Maulana Awangga. "Penerapan PCA dan Algoritma Clustering untuk Analisis Mutu Perguruan Tinggi di LLDIKTI Wilayah IV." NUANSA INFORMATIKA 18, no. 2 (2024): 67–77. http://dx.doi.org/10.25134/ilkom.v18i2.211.

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The Internal Quality Assurance System (SPMI) is a guideline used by universities to assess the quality of performance and implementation of higher education internally. SPMI is very important to be considered by universities in order to compete positively with other universities, both at home and abroad, as well as to improve the management and implementation of higher education in the institution. In this study, three machine learning algorithms are applied, namely K- Means, Mean Shift, and DBSCAN, to cluster SPMI data. The methods used include Principal Component Analysis (PCA) to reduce dat

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Sikana, Arina Mana, and Arie Wahyu Wijayanto. "Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering." Jurnal Ilmu Komputer 14, no. 2 (2021): 66. http://dx.doi.org/10.24843/jik.2021.v14.i02.p01.

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Indeks Pembangunan Manusia (IPM) merupakan indikator penting dalam pengukuran tingkat keberhasilan pembangunan kualitas hidup manusia. Pengelompokan Indeks Pembangunan Manusia (IPM) bertujuan untuk membagi wilayah-wilayah ke dalam kelompok berdasarkan Indeks Pembangunan Manusia wilayah tersebut tahun 2019. Pengelompokan Indeks Pembangunan Manusia Indonesia tahun 2019 membandingkan metode Partitioning Clustering dan Hierarchical Clustering. Algoritma Partitioning Clustering yang digunakan adalah algoritma K-Means Clustering, sedangkan algoritma Hierarchical Clustering adalah algoritma Agglomera

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Morales, Félix, Miguel García-Torres, Gustavo Velázquez, et al. "Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study." Electronics 11, no. 2 (2022): 267. http://dx.doi.org/10.3390/electronics11020267.

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Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Consid

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Lima, Suzane Pereira, and Marcelo Dib Cruz. "A genetic algorithm using Calinski-Harabasz index for automatic clustering problem." Revista Brasileira de Computação Aplicada 12, no. 3 (2020): 97–106. http://dx.doi.org/10.5335/rbca.v12i3.11117.

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Data clustering is a technique that aims to represent a dataset in clusters according to their similarities. In clustering algorithms, it is usually assumed that the number of clusters is known. Unfortunately, the optimal number of clusters is unknown for many applications. This kind of problem is called Automatic Clustering. There are several cluster validity indexes for evaluating solutions, it is known that the quality of a result is influenced by the chosen function. From this, a genetic algorithm is described in this article for the resolution of the automatic clustering using the Calinsk

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Chikumbo, Oliver, and Vincent Granville. "Optimal Clustering and Cluster Identity in Understanding High-Dimensional Data Spaces with Tightly Distributed Points." Machine Learning and Knowledge Extraction 1, no. 2 (2019): 715–44. http://dx.doi.org/10.3390/make1020042.

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The sensitivity of the elbow rule in determining an optimal number of clusters in high-dimensional spaces that are characterized by tightly distributed data points is demonstrated. The high-dimensional data samples are not artificially generated, but they are taken from a real world evolutionary many-objective optimization. They comprise of Pareto fronts from the last 10 generations of an evolutionary optimization computation with 14 objective functions. The choice for analyzing Pareto fronts is strategic, as it is squarely intended to benefit the user who only needs one solution to implement

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Saidah, Diah Aliyatus, Rukun Santoso, and Tatik Widiharih. "PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KESEHATAN LINGKUNGAN MENGGUNAKAN METODE PARTITIONING AROUND MEDOIDS DENGAN VALIDASI INDEKS INTERNAL." Jurnal Gaussian 11, no. 2 (2022): 302–12. http://dx.doi.org/10.14710/j.gauss.v11i2.35478.

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Environmental health is an important aspect in efforts to achieve public health. The condition of environmental health in Indonesia is varies in each province, so the priorities for increasing environmental health are also different. This study aims to grouping provinces in Indonesia based on environmental health indicators in order to know the high/low environmental quality in each province to assist the government in optimizing environmental health efforts. The grouping of provinces is done partitioning around medoids method which is robust to data containing outliers. The measure of similar

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Części książek na temat "Calinski-Harabasz"

Dungkratoke, Narongdech, Chantana Simtrakankul, Janejira Laomala, and Sayan Kaennakham. "The Impact of Firefly Algorithm (FA) Optimization on Gaussian Kernel-Based Fuzzy C-Means Clustering (GKFCM) Efficiency." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241403.

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This study explores the effectiveness of the traditional Firefly algorithm (FA) in optimizing the Gaussian Kernel-based Fuzzy C-means clustering (GKFCM) algorithm by adjusting ‘sigma’ and ‘m’. We compare GKFCM with FA optimization (With FA) to without it (Without FA) using the Calinski Harabasz (CH) index and the number of iterations. For all four datasets analyzed in this study, the findings consistently indicate that the GKFCM algorithm optimized with the Firefly algorithm (FA) performs substantially better than its non-optimized counterpart, achieving higher Calinski Harabasz (CH) scores an

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Anderson, Raymond A. "Practical Application." In Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0013.

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This chapter provides further practical detail where prior theoretical background was insufficient but focused on binary categorization. (1) Data transformation—i) rescaling—mathematical transformations applied directly to predictors {min-max, log/exponent, power/root, theoretical distribution &c}; ii) discretization—subject counts allow {dummy variables, weights of evidence, piecewise}. (2) Characteristic assessments—more specifics around use for discretized predictors and binary targets {weight of evidence, information value, population stability index, chi-square}. (3) Model assessments

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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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Simtrakankul, Chantana, Narongdech Dungkratoke, Janejira Laomala, and Sayan Kaennakham. "A Comparative Study of Distance Functions in Enhancing Cluster Quality Through Gaussian Kernel-Based Fuzzy C-Means." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241404.

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This study evaluates various distance functions in Gaussian Kernel-based fuzzy C-means clustering across six datasets. Key findings include the superior performance of the Cosine distance, which consistently yielded the lowest Davies-Bouldin scores, notably 0.3823 and 0.5226 for Datasets 5 and 6, and required fewer iterations to converge, with figures as low as 7 and 8. Squared Euclidean distance also showed effectiveness, particularly with fewer iterations needed for convergence, such as 22 and 41 for Datasets 1 and 3. In contrast, Chebyshev and Minkowski distances, requiring up to 119 iterat

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Zafeiropoulos, Charalampos, Ioannis N. Tzortzis, Ioannis Rallis, Eftychios Protopapadakis, Nikolaos Doulamis, and Anastasios Doulamis. "Evaluating the Usefulness of Unsupervised Monitoring in Cultural Heritage Monuments." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210086.

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In this paper, we scrutinize the effectiveness of various clustering techniques, investigating their applicability in Cultural Heritage monitoring applications. In the context of this paper, we detect the level of decomposition and corrosion on the walls of Saint Nicholas fort in Rhodes utilizing hyperspectral images. A total of 6 different clustering approaches have been evaluated over a set of 14 different orthorectified hyperspectral images. Experimental setup in this study involves K-means, Spectral, Meanshift, DBSCAN, Birch and Optics algorithms. For each of these techniques we evaluate i

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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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Şahin Şener, Ümmü, and Ersin Şener. "Clustering Analysis of European Health Status Pre-Covid-19." In Güncel Ekonometrik ve İstatistiksel Uygulamalar ile Akademik Çalışmalar. Özgür Yayınları, 2024. http://dx.doi.org/10.58830/ozgur.pub518.c2136.

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The influence of genetic factors as well as the individual's living environment and consumption habits are very important for a healthy and long life. The consumption of unhealthy products (alcohol, cigarettes, etc.) and the occurrence of diseases such as obesity and diabetes as a result of an unhealthy diet are inevitable. The aim of this study is to perform a cluster analysis by individual health criteria to observe the level of preparedness of Europe for a pandemic using data from the period just before the Covid-19 pandemic, which was announced worldwide in March 2020. The cluster analysis

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Streszczenia konferencji na temat "Calinski-Harabasz"

Lukasik, Szymon, Piotr A. Kowalski, Malgorzata Charytanowicz, and Piotr Kulczycki. "Clustering using flower pollination algorithm and Calinski-Harabasz index." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7744132.

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Ribeiro Junior, Franklin M., Reinaldo A. C. Bianchi, and Carlos A. Kamienski. "Detecção do Comportamento da Névoa em Sistemas IoT." In Workshop de Computação Urbana. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/courb.2022.223453.

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Um sistema IoT baseado em névoa contém milhares de dispositivos heterogêneos com suas próprias limitações. Este artigo propõe um sistema que utiliza aprendizado de máquina para agrupar os comportamentos desses dispositivos e identificar anomalias no desempenho dos diferentes nós de névoa. O sistema foi avaliado para diferentes comportamentos simulados, com os algoritmos MeanShift, BIRCH e K-Means. Também foram validados os agrupamentos gerados pelos índices de Silhouette, Davies-Bouldin e Calinski Harabasz, a fim de obter o modelo de dados mais acurado. O sistema identificou os comportamentos

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Wang, Yingpei, Yanhui Xu, and Tianchu Gao. "Evaluation Method of Wind Turbine Group Classification Based on Calinski Harabasz." In 2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2021. http://dx.doi.org/10.1109/ei252483.2021.9713300.

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de Araújo, Jonas Gabriel L., Thaís G. do Rêgo, and Yuri de A. M. Barbosa. "Avaliação de Algoritmos de Clusterização para Agrupamento de Descrições de Produtos em Notas Fiscais Eletrônicas." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/stil.2024.245372.

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A nota fiscal eletrônica é essencial para o processo de auditoria fiscal. Este artigo avalia a eficácia de algoritmos de clusterização para agrupar descrições de produtos em notas fiscais eletrônicas, um desafio devido à falta de padronização nos registros. Usando similaridade de strings e ajustes para unidades de medida, foram testados DBSCAN, HDBSCAN, OPTICS e Agglomerative Clustering. As métricas de avaliação incluíram o Coeficiente de Silhueta, Índice de Calinski-Harabasz e a porcentagem de produtos agrupados. O HDBSCAN apresentou o melhor desempenho inicial, e a subclusterização, apesar d

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Maciel, Felipe Anderson O., Antonio Rafael Braga, Alisson de Lima e. Silva, Ticiana L. Coelho da Silva, Breno M. Freitas, and Danielo G. Gomes. "Reconhecimento de Padrões de Colônias de Abelhas Apis Mellifera Segundo Mudanças das Estações do Ano." In IX Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/wcama.2018.2937.

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Na qualidade de principal agente polinizador, as abelhas são essenciais à produção de alimentos para o ser humano e para manutenção dos ecossistemas. Entre as culturas agrícolas utilizadas para o consumo humano, 75% dependem de polinização. Alinhando-se a uma preocupação atual com a sobrevivência das abelhas, este artigo visa descobrir estados de colônias de Apis mellifera a fim de auxiliar o apicultor no manejo e na manutenção de suas colmeias. Nossa metodologia consistiu na aplicação de uma técnica de clusterização em dois datasets reais de colmeias com dados de temperatura, umidade e massa.

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Chreim, Zahraa, Hussein Hazimeh, Hassan Harb, et al. "Reduce++: Unsupervised Content-Based Approach for Duplicate Result Detection in Search Engines." In International Conference on Signal Processing and Vision. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122211.

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Search engines are among the most popular web services on the World Wide Web. They facilitate the process of finding information using a query-result mechanism. However, results returned by search engines contain many duplications. In this paper, we introduce a new content-type-based similarity computation method to address this problem. Our approach divides the webpage into different types of content, such as title, subtitles, body, etc. Then, we find for each type a suitable similarity measure. Next, we add the different calculated similarity scores to get the final similarity score between

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Shen, Yan, Yue Tang, and Qian Dou. "The Discovery of Micro-Blog Users' Interest Groups Based on SSLOK-Means Clustering Focus on Big Data Improved by Calinski-Harabasz." In 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2018. http://dx.doi.org/10.1109/icdsba.2018.00025.

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Mahmudan, Ali, Di Asih I. Maruddani, and Budi Warsito. "Optimization of hierarchical clustering method using Calinski-Harabasz Pseudo F-statistic for clustering district/city in Central Java Province based on education indicators." In ADVANCES IN INTELLIGENT APPLICATIONS AND INNOVATIVE APPROACH. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0140170.

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Magacho, Heitor G. B., Wagner R. Telles, and Marcos Bedo. "Pré-processamento de dados para Modelos Hidrológicos com o algoritmo k-Medoids: O caso do Rio Pomba." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbbd_estendido.2022.21835.

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Esse estudo propõe a aplicação do algoritmo k-medoids como etapa de pré-processamento para reduzir a cardinalidade de conjuntos de dados topológicos em simulações hidrológicas do mundo real. Assim, é importante que os medóides identificados sejam representativos para o problema simulado e que os dados reduzidos não prejudiquem a qualidade dos Modelos Hidrológicos executados na sequência de processamento. Em particular, esse trabalho investiga o uso do pré-processamento junto a dois tipos de Modelos Digitais de Terreno para bacias hidrográficas no software MOHID Studio. Como estudo de caso, foi

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Gonzalez, Keyla, and Siddharth Misra. "Rapid Time-Lapse Monitoring of Geological Carbon Storage." In SPE EuropEC - Europe Energy Conference featured at the 84th EAGE Annual Conference & Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214405-ms.

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Abstract Precision monitoring of the subsurface carbon-dioxide plume ensures long-term, sustainable geological carbon storage at a large scale. Electrical resistivity tomography (ERT) can accurately map the evolution of the CO2 saturation during geological carbon storage. To better monitor the CO2 plume migration in a storage reservoir, we develop an unsupervised spatiotemporal clustering to process the CO2 saturation maps derived from the ERT measurements acquired over 80 days. Using dynamic time wrapping (DTW) Kmeans clustering, four distinct clusters were identified in the CO2-storage reser

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