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Journal articles on the topic 'Calinski-Harabasz'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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BAYRAM, Muhammed Ali, and Cemil Közkurt. "Comparison of K-Means, Fuzzy C-Means and Fuzzy Logic Based Clustering Algorithms for Customer Segmentation." Aintelia Science Notes 3, no. 1 (2024): 29–40. https://doi.org/10.5281/zenodo.14556226.

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Customer segmentation is a critical tool for businesses to optimize their marketing strategies and increase customer loyalty. This study aims to segment customers based on their buying behavior using K-Means, Fuzzy C-Means (FCM) and Fuzzy Logic based clustering algorithms. While the K-Means algorithm divides the data into a certain number of clusters and determines a center for each cluster, the FCM algorithm allows data points to belong to more than one cluster with varying degrees of membership. Fuzzy Logic based clustering provides a more nuanced segmentation by using a system of fuzzy memb

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Hasan, Fatima M., Talal F. Hussein, Hanadi D. Saleem, and Omar S. Qasim. "Enhanced Unsupervised Feature Selection Method Using Crow Search Algorithm and Calinski-Harabasz." International Journal of Computational Methods and Experimental Measurements 12, no. 2 (2024): 185–90. http://dx.doi.org/10.18280/ijcmem.120208.

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Aik, Lim Eng, Tan Wee Choon, and Mohd Syafarudy Abu. "K-means Algorithm Based on Flower Pollination Algorithm and Calinski-Harabasz Index." Journal of Physics: Conference Series 2643, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1742-6596/2643/1/012019.

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Abstract Aiming at the problems that the Flower Pollination (FP) algorithm is easy to fall into the local optimum, the searchability is weak, and the k-means algorithm is easily affected by the selection of the initial clustering centre, a k-means algorithm based on the FP algorithm is proposed. Six benchmark functions test the improved FP algorithm. The effectiveness of the k-means algorithm based on the improved FP algorithm was tested and verified with UCI machine learning and artificial datasets. The verification results showed that the improved FP algorithm improved based on ensuring a fa

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Ulummuddin, Ikhya, Anggraini Puspita Sari, and Made Hanindia Prami Swari. "Pemanfaatan Data Ulasan Pengguna untuk Membangun Sistem Klasterisasi berdasarkan Pain Points menggunakan Algoritma K-Means." Jurnal Teknologi Terpadu 10, no. 1 (2024): 70–76. http://dx.doi.org/10.54914/jtt.v10i1.1252.

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Dalam design thinking, tahap empathize dan define termasuk bagian UX research. Tujuannya untuk menganalisis pain points atau keluhan yang dialami oleh para pengguna dari data kualitatif yang didapatkan. Namun, proses ini selalu dilakukan secara manual, yang mana dapat memakan waktu dan sumber daya yang lebih banyak. Tujuan dari penelitian ini adalah mengembangkan sistem pengelompokkan data kualitatif berdasarkan topik masalah menggunakan K-Means clustering dan beberapa metode evaluasi, yaitu silhouette score, Davies-Bouldin Index, dan Calinski-Harabasz Index dengan bahasa pemrograman Python ya

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Fadhillah, Muhamad Fikri, Aldo Lovely Arief Suyoso, and Ira Puspitasari. "Segmentasi Pelanggan dengan Algoritma Clustering Berdasarkan Atribut Recency, Frequency dan Monetary (RFM)." MALCOM: Indonesian Journal of Machine Learning and Computer Science 5, no. 1 (2024): 48–56. https://doi.org/10.57152/malcom.v5i1.1491.

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Perdagagan bebas yang disepakati dengan negara maju menimbulkan perubahan karakter konsumen di Indonesia [1] ditambah efek pandemi Covid-19 semua komoditi dalam negeri hampir mengalami penuruan penjualan dan laba usaha tiap tahunnya[2]. Perusahaan harus mengubah strategi lain dalam menarik pelanggan menggunakan data karakteristik pelanggan yang berbeda-beda salah satunya data transaksi penjualan [3]. Data tersebut nantinya akan disegmentasi dengan metode clustering. Clustering adalah teknik analisis data yang bertujuan mengelompokkan objek-objek ke dalam grup atau klaster berdasarkan kesamaan

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Airlangga, Gregorius. "A Comparative Analysis of Clustering Algorithms for Expedia’s Travel Dataset." sinkron 9, no. 1 (2025): 476–83. https://doi.org/10.33395/sinkron.v9i1.14343.

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The effective segmentation of travel data is crucial for deriving actionable insights in the tourism and hospitality sectors. This study conducts a comprehensive evaluation of four clustering algorithms Agglomerative Clustering, DBSCAN, Gaussian Mixture Models (GMM), and KMeans on a travel dataset, using three widely recognized metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Score. The dataset was preprocessed through standardization and dimensionality reduction via Principal Component Analysis (PCA) to facilitate visualization and ensure computational efficiency. The re

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Doi, Maria Dafrosa, Agus Rusgiyono, and Triastuti Wuryandari. "ANALISIS k-MEDOIDS DENGAN VALIDASI INDEKS PADA IPM DAERAH 3T DI INDONESIA." Jurnal Gaussian 12, no. 2 (2023): 178–88. http://dx.doi.org/10.14710/j.gauss.12.2.178-188.

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Human development is a development paradigm that places humans as the main target of all development activities, namely controling over resources, improving health and improving education. The Human Development Index (HDI) in Indonesia varies in each district, especially in the 3T areas. The 3T area is an area that is classified as underdeveloped, remote and outermost in terms of economy, health, education and infrastructure. The k-Medoids method is a partitional clustering method for grouping several objects into clusters. This clustering algorithm uses the medoid as the center of the cluster

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Laskowski, Jan Franciszek, and Paweł Tomiło. "A new AI-based method for clustering survey responses." Journal of Modern Science 54, no. 5 (2023): 355–77. http://dx.doi.org/10.13166/jms/176171.

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ObjectivesMany research projects, particularly in social science research, depend on clustering survey responses. When analyzing survey data, traditional clustering algorithms have several drawbacks. The ability to analyze survey data more effectively has been made possible by recent developments in artificial intelligence (AI) and machine learning (ML). The aim of this article is to present a new, AI-based method of clustering survey responses using a Variational Autoencoder (VAE).Material and methodsTo determine the effectiveness of grouping, the new VAE clustering method was compared with K

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Karim, Md Razaul, and Zekai Cheng. "COMPARATIVE STUDY OF CLUSTERING ALGORITHMS FOR STUDENT PERFORMANCE EVALUATION." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 424–32. https://doi.org/10.5281/zenodo.15511569.

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Predicting student performance is essential for enhancing educational outcomes, enabling educators to identify studentswho may need additional support or intervention. Clustering algorithms, as unsupervised data mining techniques, areparticularly effective at uncovering patterns in student performance data. These algorithms can group students basedon their exam scores, providing insights that allow for more tailored and targeted educational strategies. This studycompares four unsupervised methods K-Means, DBSCAN, Hierarchical Clustering (Ward linkage), and GaussianMixture Models (GMM) on a dat

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Andayani, Sri, Namita Retnani, Thesa Adi Saputra Yusri, and Bambang Sumarno Hadi Marwoto. "A COMPARATIVE ANALYSIS OF DBSCAN AND GAUSSIAN MIXTURE MODEL FOR CLUSTERING INDONESIAN PROVINCES BASED ON SOCIOECONOMIC WELFARE INDICATORS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 3 (2025): 2039–56. https://doi.org/10.30598/barekengvol19iss3pp2039-2056.

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Public welfare refers to a condition in which people experience happiness, comfort, prosperity, and can adequately fulfill their basic needs. Indonesia consists of several provinces, each with varying levels of welfare. One crucial aspect in promoting equitable development is ensuring that all regions in Indonesia achieve similar welfare standards. This study aims to classify Indonesian provinces based on socioeconomic welfare indicators, with the results serving as a basis for policy-making that considers regional potential and challenges. The data used in this study are secondary data obtain

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Ryando, M. Bucci, and Sutarman Sutarman. "Clustering of Eligibility and Characteristics of Smart Indonesia Card Recipients for College using Agglomerative Hierarchical Clustering." JURNAL SISFOTEK GLOBAL 14, no. 2 (2024): 137. http://dx.doi.org/10.38101/sisfotek.v14i2.15707.

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Since 2023, the Smart Indonesia Card or Kartu Indonesia Pintar-Kuliah (KIP-K) has two schemes. Consisting of KIP-K Scheme 1, where scholarship recipients are exempted from tuition fees and receive living allowances during the KIP-K scholarship period and KIP-K Scheme 2, where scholarship recipients are only exempted from tuition fees without receiving living allowances. Lack of information about the characteristics of prospective KIP-K scholarship recipients is a problem in itself. Many universities/institutions are not on target in providing this scholarship. This study aims to obtain cluster

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Ajeng Arifa Chantika Rindu, Ria Astriratma, and Ati Zaidiah. "K-Means Algorithm Implementation for Project Health Clustering." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 5 (2023): 1064–76. http://dx.doi.org/10.29207/resti.v7i5.5181.

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Indonesia has several companies that are engaged in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. A project’s potential can boost the company’s revenue and productivity. On the other hand, there are some risks that need to be considered for every project when it is about to start. Project data is recorded from start to finish so that the project's progress and improvements can be monitored and analyzed. As the project runs, the project team at one of Indonesia's telecommunication companies, which is responsible for the p

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Sun, Yi, Jun Zheng, Lingjuan Lyn, et al. "The Same Name Is Not Always the Same: Correlating and Tracing Forgery Methods across Various Deepfake Datasets." Electronics 12, no. 11 (2023): 2353. http://dx.doi.org/10.3390/electronics12112353.

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Deepfakes are becoming increasingly ubiquitous, particularly in facial manipulation. Numerous researchers and companies have released multiple datasets of face deepfakes labeled to indicate different methods of forgery. However, naming these labels is often arbitrary and inconsistent, leading to the fact that most researchers now choose to use only one of the datasets for research work. However, researchers must use these datasets in practical applications and conduct traceability research. In this study, we employ some models to extract forgery features from various deepfake datasets and util

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Valles-Coral, Miguel, Ulises Lazo-Bartra, Lloy Pinedo, et al. "Algoritmo no supervisado para clasificar niveles de riesgo de inmigración." Data and Metadata 2 (November 16, 2023): 98. http://dx.doi.org/10.56294/dm202398.

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Introducción: la migración es un fenómeno social que afecta la estructura y distribución de la población, siendo motivada por la búsqueda de mejores oportunidades y condiciones de vida. En tal sentido, la migración irregular representa un desafío para los países receptores, dado que conlleva la entrada de individuos sin la documentación correspondiente, pudiendo comprometer la seguridad nacional y el control fronterizo de los países.Objetivo: evaluar la aplicación del algoritmo no supervisado DBSCAN para clasificar a extranjeros según el nivel de riesgo de inmigración irregular en la Superinte

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Wang, Xu, and Yusheng Xu. "An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index." IOP Conference Series: Materials Science and Engineering 569 (August 9, 2019): 052024. http://dx.doi.org/10.1088/1757-899x/569/5/052024.

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Maulina, Mita Nourma, Mustafid Mustafid, and Bagus Arya Saputra. "OPTIMALISASI CLUSTER PADA CLUSTER HIERARKI MENGGUNAKAN PSEUDO F-STATISTIC CALINSKI HARABASZ UNTUK KETAHANAN PANGAN." Jurnal Gaussian 14, no. 1 (2025): 01–12. https://doi.org/10.14710/j.gauss.14.1.01-12.

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Food security is the ability of a nation to ensure that all of its inhabitants obtain food in sufficient quantities, of proper quality, based on optimizing the utilization of food. The food security of each region is different, resulting a region to have low or high food security, one of which is the area in Pati Regency as a research area. To find out this, area clustering was carried out based on variables of planted area, food production, number of food supply facilities and infrastructure, number of people with low welfare levels, number of sub-districts without sufficient connecting acces

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Song, Liming, Zhimin Chen, XinXin Meng, and Shuai Kang. "Cluster analysis of time series K-Means line loss based on DWT distance." Journal of Physics: Conference Series 2137, no. 1 (2021): 012063. http://dx.doi.org/10.1088/1742-6596/2137/1/012063.

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Abstract This paper constructs an indicator system composed of inherent attributes and time characteristics of the line based on the line loss, and proposes a K-Means line loss cluster analysis model based on this indicator system. The line is classified according to the clustering results. The result is 314.51 on the CH index (Calinski Harabasz Index), 0.19 on the Silhouette Cofficient (Silhouette Cofficient), and a running time of 0.508s. Compared with the traditional algorithm, it is greatly improved. The field of line loss analysis has guiding significance.

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Ostapenko, R. O., and I. A. Hodashinsky. "Setting a rule base for a fuzzy classifier using the grasshopper optimization algorithm and the clustering algorithm." Proceedings of Tomsk State University of Control Systems and Radioelectronics 25, no. 2 (2022): 31–36. http://dx.doi.org/10.21293/1818-0442-2022-25-2-31-36.

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The article presents a description of a hybrid algorithm for generating fuzzy rules for a fuzzy classifier using grasshopper optimization algorithm and the K-means data clustering algorithm. The performance of clustering was evaluated by three fitness functions: total variance, Davis–Bouldin index, and Calinski–Harabasz index. Triangular and Gaussian membership functions have been investigated. The efficiency of the generated fuzzy rule bases has been tested on real datasets. The best combination is to use the total variance as the fitness function and the Gaussian function as the membership f

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Li, Jiada, Daniyal Hassan, Simon Brewer, and Robert Sitzenfrei. "Is Clustering Time-Series Water Depth Useful? An Exploratory Study for Flooding Detection in Urban Drainage Systems." Water 12, no. 9 (2020): 2433. http://dx.doi.org/10.3390/w12092433.

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As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have drawn tremendous interest in event detection and hydraulic water level and flow prediction recently. However, most of them are applied in water distribution systems and few studies consider using unsupervised cluster analysis to group the time-series hydraulic-hydrologic data in stormwater urban drainage systems. To improve the understanding of how cluster analysis contributes to flooding location detection, this study compared the performance of K-means clustering, agglomerative clu

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Lew, Si-Hyung, Ye-Jin Jeong, Ye-Ri Roh, and Dong-Ho Kang. "Exploratory Cluster-Based Radiographic Phenotyping of Degenerative Cervical Disorder: A Retrospective Study." Medicina 61, no. 5 (2025): 916. https://doi.org/10.3390/medicina61050916.

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Background and Objectives: Degenerative cervical myelopathy (DCM), a major subtype of degenerative cervical disorders, presents with diverse sagittal alignment patterns. However, radiography-based phenotyping remains underexplored. This study aimed to identify distinct cervical alignment subgroups using unsupervised clustering analysis and to explore their potential clinical relevance. Materials and Methods: We analyzed 1371 lateral cervical radiographs of patients with DCM. C3–C7 sagittal vertical axis (SVA), lordosis, vertical length, and curved length were determined. K-means clustering was

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Alwani, Nadira Nisa, Megawati Megawati, Anwar Fitrianto, Erfiani Erfiani, and Alfa Nugraha Pradana. "Agglomerative Nesting Cluster Analyst in Mapping District/City Health Facilities in West Java Province." Jurnal Matematika, Statistika dan Komputasi 20, no. 3 (2024): 484–96. http://dx.doi.org/10.20956/j.v20i3.32043.

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The use of Hierarchical Clustering is used to group districts or cities in West Java according to the number of health facilities, distance to health facilities and population density using Agglomerative Nesting (AGNES). Clustering in this study utilizes complete linkage clustering. The elbow method produces two optimal clusters which are then validated with the sillhoute coefficient and Calinski-Harabasz. In this study, there are 27 variables in the form of health facilities spread across 27 regencies/cities in West Java in 2021. The results of the cluster analysis formed in this study are 18

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Simić, Svetlana, José R. Villar, José Luis Calvo-Rolle, Slobodan R. Sekulić, Svetislav D. Simić, and Dragan Simić. "An Application of a Hybrid Intelligent System for Diagnosing Primary Headaches." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 1890. http://dx.doi.org/10.3390/ijerph18041890.

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(1) Background: Modern medicine generates a great deal of information that stored in medical databases. Simultaneously, extracting useful knowledge and making scientific decisions for diagnosis and treatment of diseases becomes increasingly necessary. Headache disorders are the most prevalent of all the neurological conditions. Headaches have not only medical but also great socioeconomic significance. The aim of this research is to develop an intelligent system for diagnosing primary headache disorders. (2) Methods: This research applied various mathematical, statistical and artificial intelli

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Laurenso, Justin, Danny Jiustian, Felix Fernando, Vartin Suhandi, and Theresia Herlina Rochadiani. "Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia." Journal of Applied Informatics and Computing 8, no. 1 (2024): 62–70. http://dx.doi.org/10.30871/jaic.v8i1.4871.

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In today's era, smoking is a common thing in everyday life. Along with the development of the times, an innovation emerged, namely the electric cigarette or vape. Electric cigarettes or vapes use electricity to produce vapor. The e-cigarette business is very promising in today's business world due to the consistent increase in market demand. However, determining the target buyer is one of the things that is quite important in determining the success of a business. In this analysis, the background of each region in Indonesia has different diversity; therefore, observation of data is needed to f

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Nabawi, Isnan, Kusrini, and Sudarmawan. "Klastering Spesifikasi Smartphone Menggunakan Algoritma Self Organizing Map." Jurnal Informatika Komputer, Bisnis dan Manajemen 21, no. 1 (2023): 35–44. http://dx.doi.org/10.61805/fahma.v21i1.23.

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Smartphone merupakan alat komunikasi modern yang saat ini sudah banyak dimiliki oleh masyarakat Indonesia. Tidak ada pengelompokan yang pasti dari banyaknya smartphone yang tersebar saat ini sehingga konsumen cukup kesulitan memilih smartphone dan produsen pun perlu mengelompokkan sendiri pasarnya. Pengelompokan smartphone dirasa perlu untuk membantu konsumen untuk mengetahui kelas smartphone yang akan dibelinya berdasarkan spesifikasi yang diinginkan. Pengelompokan pada penelitian ini menggunakan metode Self-Organizing Maps yang dieksekusi menggunakan Bahasa pemrograman Python. Hasil pengelom

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Cengizler, Caglar, and M. Kerem-Un. "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. http://dx.doi.org/10.9734/bjmcs/2017/33729.

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Đồng, Nguyễn Như, та Phan Thành Huấn. "CẢI TIẾN THUẬT TOÁN PHÂN CỤM MỜ DỰA TRÊN ĐỘ ĐO TRỌNG SỐ ENTROPY VÀ CHỈ SỐ CALINSKI-HARABASZ". Tạp chí Khoa học Đại học Đà Lạt 8, № 2 (2018): 13. http://dx.doi.org/10.37569/dalatuniversity.8.2.408(2018).

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Phân cụm là kỹ thuật quan trọng trong khai thác dữ liệu và được ứng dụng rộng rãi trong các lĩnh vực như nhận dạng mẫu, thị giác máy tính và điều khiển mờ. Trong bài viết này, chúng tôi trình bày thuật toán cải tiến phân cụm mờ dựa vào sự kết hợp thuật toán phân cụm mờ dựa trên độ đo trọng số Entropy và chỉ số Calinski-Harabasz. Ưu điểm của phương pháp này là không những phân chia cụm hiệu quả, có độ chính xác cao mà còn có khả năng đo lường cụm, đánh giá cụm nhằm tìm ra được số cụm tối ưu đủ đáp ứng cho các nhu cầu thực tiễn. Sau cùng, chúng tôi trình bày kết quả thực nghiệm trên dữ liệu thực

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Ashari, Ilham Firman, Eko Dwi Nugroho, Randi Baraku, Ilham Novri Yanda, and Ridho Liwardana. "Analysis of Elbow, Silhouette, Davies-Bouldin, Calinski-Harabasz, and Rand-Index Evaluation on K-Means Algorithm for Classifying Flood-Affected Areas in Jakarta." Journal of Applied Informatics and Computing 7, no. 1 (2023): 89–97. http://dx.doi.org/10.30871/jaic.v7i1.4947.

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Jakarta is the capital city of Indonesia, which has a high population density, and is an area that is frequently hit by floods. This study aims to determine the classification of flood-affected areas in Jakarta between severe, moderate, and low. Design/method/approach: The study was conducted using the elbow, Silhouette, Davidson-Bouldin, and Calinski-Harabasz methods on the K-means algorithm, as well as the Rand method. index for evaluation. Grouping with 3 and 6 groups is the best grouping value based on Calinski-Harabasz. By using the davies bouldin index from the observations, the K value

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CHERRADI, Mohamed. "Exploration of Scientific Documents through Unsupervised Learning-Based Segmentation Techniques." Seminars in Medical Writing and Education 3 (April 1, 2024): 68. http://dx.doi.org/10.56294/mw202468.

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Navigating the extensive landscape of scientific literature presents a significant challenge, prompting the development of innovative methodologies for efficient exploration. Our study introduces a pioneering approach for unsupervised segmentation, aimed at revealing thematic trends within articles and enhancing the accessibility of scientific knowledge. Leveraging three prominent clustering algorithms—K-Means, Hierarchical Agglomerative, and DBSCAN—we demonstrate their proficiency in generating meaningful clusters, validated through assessment metrics including Silhouette Score, Calinski-Hara

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Erlisa Santri and Dede Brahma. "Analysis Of Poverty Level Mapping In Riau Province Using The K-Means Method." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 3, no. 2 (2024): 534–38. http://dx.doi.org/10.59934/jaiea.v3i2.414.

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This research aims to understand the pattern of poverty distribution in Riau Province, identify clusters that reflect similar characteristics and provide a basis for developing more targeted policies. This approach uses machine learning techniques, especially the K-Means algorithm, to form clusters based on poverty level data. The results of the analysis show cluster 0 (C0) with a high poverty level and cluster 1 (C1) with a low poverty level. K-Means proved effective in grouping areas with similar levels of poverty, and provided a strong foundation for further analysis. Evaluation results usi

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Kartal, Burcu. "Zaman Serisi Kümeleme Yöntemi ile Küresel Cinsiyet Uçurumu Endeksinin Analizi." Uluslararası Ekonomi İşletme ve Politika Dergisi 9, no. 1 (2025): 420–37. https://doi.org/10.29216/ueip.1633127.

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Bu çalışma, dünya ülkelerinin Küresel Cinsiyet Uçurumu Endeksi (Global Gender Gap Index- GGGI) temelinde kümeleme analizini amaçlamaktadır. Bu kapsamda, 2006-2024 yıllarını kapsayan GGGI verileri derlenmiş ve 18 yıllık döneme ait zaman serisi verileri kullanılarak 99 ülkenin kümeleme analizi gerçekleştirilmiştir. Ülkeler arasındaki benzerliklerin belirlenmesi için uzaklık ölçütü olarak Dinamik Zaman Bükmesi (Dynamic Time Warping-DTW) yöntemi kullanılmıştır. Kümeleme analizinde hiyerarşik kümeleme (tek bağlantı, tam bağlantı ve Ward’s yöntemi), k-medoid kümeleme (Partitioning Around Medoids - P

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Saleki, Meisam, Kamran Mostafaei, Zakaria Bin Endut, and Mohammad Nabi Kianpour. "ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH." Rudarsko-geološko-naftni zbornik 40, no. 3 (2025): 131–45. https://doi.org/10.17794/rgn.2025.3.10.

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This study explores the application of advanced clustering techniques—Spectral Clustering, Gaussian Mixture Models (GMM), and a hybrid approach combining Autoencoders with K-Means—to classify tin deposits in Malaysia. Geochemical data from 28 tin samples across regions such as Pengkalan Hulu North, Menglembu, Klian Intan, and Sungai Lembing were analysed to identify distinct mineralization patterns. The results revealed that the integration of Autoencoders with K-Means yielded the highest clustering quality, with a Silhouette Score above 0.4 and a Calinski-Harabasz Index of 90 at four clusters

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P, Sudhasini, and Ashadevi B. "Cluster Predictive Model Using Affinity Propagation Algorithm to Group Mushroom 5.8s rRNA Sequences." Indian Journal of Science and Technology 15, no. 41 (2022): 2129–42. https://doi.org/10.17485/IJST/v15i41.1341.

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Abstract <strong>Background:</strong> The main emphasis of the article is biological information on a distinct species of mushroom (Phylum Basidiomycota) data collection of 5.8s rRNA sequences. Macrofungi from the phylum Basidiomycota are predominantly used as therapeutic mushrooms in several countries. During the rainy season, hundreds of macrofungal basidiocarps were discovered in Tamilnadu. The internal transcribed spacer (ITS) and 5.8S rRNA gene sequence markers, which have been collected from NCBI, were used to isolate at least thirty of these strains that fall under the Basidiomycot

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Wu, Zengyuan, Lingmin Jin, Jiali Zhao, Lizheng Jing, and Liang Chen. "Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm." Computational Intelligence and Neuroscience 2022 (June 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/9930613.

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In view of the shortcomings of traditional clustering algorithms in feature selection and clustering effect, an improved Recency, Frequency, and Money (RFM) model is introduced, and an improved K-medoids algorithm is proposed. Above model and algorithm are employed to segment customers of e-commerce. First, traditional RFM model is improved by adding two features of customer consumption behavior. Second, in order to overcome the defect of setting K value artificially in traditional K-medoids algorithm, the Calinski–Harabasz (CH) index is introduced to determine the optimal number of clustering

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Ahmadov, Eltun. "COMPARATIVE ANALYSIS OF K-MEANS AND FUZZY C-MEANS ALGORITHMS ON DEMOGRAPHIC DATA USING THE PCA METHOD." Problems of Information Technology 14, no. 1 (2023): 15–22. http://dx.doi.org/10.25045/jpit.v14.i1.03.

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The concept of demography, which includes the processes such as birth, death, natural increase, improvement of employment and standard of living of the population, migration, etc., occupies a unique place in the global processes of the modern era. In this regard, this article uses clustering algorithms, which are estimated as a demographic data mining technology. For the analysis of demographic data, experiments are performed using k-means and fuzzy c-means clustering algorithms in the Python programming language. The experiment uses PCA method to reduce the dimension and get more effective re

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Syahputri, Ziana, Sutarman Sutarman, and Machrani Adi Putri Siregar. "Determining The Optimal Number of K-Means Clusters Using The Calinski Harabasz Index and Krzanowski and Lai Index Methods for Groupsing Flood Prone Areas In North Sumatra." Sinkron 9, no. 1 (2024): 571–80. http://dx.doi.org/10.33395/sinkron.v9i1.13246.

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The k-means algorithm is a partitional clustering method. K-Means has several advantages, including being easy to implement, having a high level of convergence and producing denser clusters. Meanwhile, the drawback is that it is difficult to determine the optimal number of clusters. The K-Means method will be used to solve problems in areas prone to flood disasters in North Sumatra. This research aims to find the optimal number of clusters with the Calinski Harabasz Index and Krzanowski And Lai Index based on the Cluster Tightness Measure (CTM) value. There are eleven variables used in this re

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Yudhianto, Rachmat Bintang, Fajar Athallah Yusuf, Anwar Fitrianto, and L. M. Risman Dwi Jumansyah. "Analisis Visual dan Karakteristik Klub Sepakbola Liga Inggris Berdasarkan Pola Permainan Menggunakan K-Means Clustering." Jurnal Informatika Universitas Pamulang 9, no. 3 (2024): 126–35. https://doi.org/10.32493/informatika.v9i3.44640.

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This research aimed to analyze and cluster football teams in the English Premier League (EPL) for the 2023/2024 season based on their playing characteristics using K-Means clustering. Understanding the playing styles is essential for optimizing strategies and enhancing team performance. Preprocessing steps included data cleaning, feature engineering, and visualization of key features such as goals, shots, and attacking attempts. Four clusters were identified using the Elbow method, representing teams with varying levels of attacking and defensive capabilities. Evaluation of the clustering resu

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Lorenc, Augustyn, Małgorzata Kuźnar, and Tone Lerher. "Solving product allocation problem (PAP) by using ANN and clustering." FME Transactions 49, no. 1 (2021): 206–13. http://dx.doi.org/10.5937/fme2101206l.

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Proper planning of a warehouse layout and the product allocation in it, constitute major challenges for companies. In the paper, the new approach for the classification of the problem is presented. Authors used real picking data from the Warehouse Management System (WMS) from peak season from September to January. Artificial Neural Network (ANN) and automatic clustering by using Calinski-Harabasz criterion were used to develop a new classification approach. Based on the picking list the clients' orders were prepared and analyzed. These orders were used as input data to ANN and clustering. In t

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Rudianto, Regita Dewanti, and Arie Wahyu Wijayanto. "Analisis Perbandingan K-Means dan K-Medoids dalam Pengelompokan Provinsi Berdasarkan Indeks Demokrasi Indonesia 2021." Komputika : Jurnal Sistem Komputer 13, no. 1 (2024): 19–26. http://dx.doi.org/10.34010/komputika.v13i1.10812.

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Metode clustering merupakan salah satu metode dalam data mining dan berguna dalam mengelompokkan amatan yang tidak memiliki target/kelas. Salah satu analisis yang dapat dilakukan dari clustering ini adalah pengelompokan 34 provinsi di Indonesia berdasarkan aspek-aspek dalam Indeks Demokrasi Indonesia (IDI) Tahun 2021. Aspek-aspek dalam IDI tersebut di antaranya, Aspek Kebebasan, Aspek Kesetaraan, dan Aspek Kapasitas Lembaga Demokrasi. Analisis clustering perlu dilakukan untuk mengetahui pengelompokan aspek IDI beserta karakteristiknya. Metode clustering yang digunakan dalam penelitian ini yait

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Nogueira, Igor Vasconcelos, and Leticia Pedroso Ramos. "Análise da formação acadêmica dos docentes do Instituto Federal de Educação Ciência e Tecnologia de São Paulo (IFSP), por meio do uso de técnicas multivariadas." Revista Ciência em Evidência 5, FC (2024): e024004. http://dx.doi.org/10.47734/rce.v5ifc.2567.

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A formação acadêmica dos docentes desempenha um papel crucial na qualidade da educação formal, dada a relação direta entre a qualidade da educação e a qualificação dos educadores. Este estudo tem como objetivo caracterizar e agrupar os campi do IFSP em relação aos níveis de formação acadêmica dos docentes. Utilizaram-se técnicas estatísticas multivariadas, incluindo análise de componentes principais, análise fatorial e análise de agrupamento, para fornecer uma visão abrangente do ambiente educacional. Foram analisadas quatro variáveis: graduação, especialização, mestrado e doutorado. Os result

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Gustriansyah, Rendra, Juhaini Alie, and Nazori Suhandi. "A Hybrid Machine Learning Model for Market Clustering." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18824–28. https://doi.org/10.48084/etasr.9259.

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Market clustering is increasingly important for companies to understand consumer shopping behavior in the context of complex data. This study aims to develop a hybrid model that integrates Principal Component Analysis (PCA) and k-medoids to enhance market clustering based on consumer shopping patterns. The methods used include data preprocessing, PCA application for dimensionality reduction, and clustering using k-medoids. The quality of the clusters is evaluated with various validity indices. The results show that the hybrid model produces clusters with better quality compared to the single k

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