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Journal articles on the topic 'KMeans Clustering Method'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Musdar, Izmy Alwiah, and Azhari Azhari. "RCE-Kmeans Method for Data Clustering." International Journal of Advances in Intelligent Informatics 1, no. 2 (2015): 107. http://dx.doi.org/10.26555/ijain.v1i2.38.

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There have been many methods developed to solve the clustering problem. One of them is method in swarm intelligence field such as Particle Swarm Optimization (PSO). Rapid Centroid Estimation (RCE) is a method of clustering based Particle Swarm Optimization. RCE, like other variants of PSO clustering, does not depend on initial cluster centers. Moreover, RCE has faster computational time than the previous method like PSC and mPSC. However, RCE has higher standar deviation value than PSC and mPSC in which has impact in the variance of clustering result. It is happaned because of improper equilib

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Lin, Xiaohui, and Jianmin Xu. "Road network partitioning method based on Canopy-Kmeans clustering algorithm." Archives of Transport 54, no. 2 (2020): 95–106. http://dx.doi.org/10.5604/01.3001.0014.2970.

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With the increasing scope of traffic signal control, in order to improve the stability and flexibility of the traffic control system, it is necessary to rationally divide the road network according to the structure of the road network and the characteristics of traffic flow. However, road network partition can be regarded as a clustering process of the division of road segments with similar attributes, and thus, the clustering algorithm can be used to divide the sub-areas of road network, but when Kmeans clustering algorithm is used in road network partitioning, it is easy to fall into the loc

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Musdar, Izmy Alwiah, and Azhari SN. "Metode RCE-Kmeans untuk Clustering Data." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, no. 2 (2015): 157. http://dx.doi.org/10.22146/ijccs.7544.

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AbstrakTelah banyak metode yang dikembangkan untuk memecahkan berbagai masalah clustering. Salah satunya menggunakan metode-metode dari bidang kecerdasan kelompok seperti Particle Swarm Optimization (PSO). Metode Rapid Centroid Estimation (RCE) merupakan salah satu metode clustering yang berbasis PSO. RCE, seperti varian PSO clustering lainnya, memiliki kelebihan yaitu hasil clustering tidak tergantung pada inisialisasi pusat cluster awal. RCE juga memiliki waktu komputasi yang jauh lebih cepat dibandingkan dengan metode sebelumnya yaitu Particle Swarm Clustering (PSC) dan modified Particle Sw

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Huang, Wenna, Yong Peng, Yuan Ge, and Wanzeng Kong. "A new Kmeans clustering model and its generalization achieved by joint spectral embedding and rotation." PeerJ Computer Science 7 (March 30, 2021): e450. http://dx.doi.org/10.7717/peerj-cs.450.

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The Kmeans clustering and spectral clustering are two popular clustering methods for grouping similar data points together according to their similarities. However, the performance of Kmeans clustering might be quite unstable due to the random initialization of the cluster centroids. Generally, spectral clustering methods employ a two-step strategy of spectral embedding and discretization postprocessing to obtain the cluster assignment, which easily lead to far deviation from true discrete solution during the postprocessing process. In this paper, based on the connection between the Kmeans clu

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Zhu-Juan Ma, Zhu-Juan Ma, Zi-Han Wang Zhu-Juan Ma, Xiang-Hua Chen Zi-Han Wang, and Feng Liu Xiang-Hua Chen. "DP-Kmeans and Beyond: Optimal Clustering with a new Clustering Validity Index." 電腦學刊 33, no. 5 (2022): 001–17. http://dx.doi.org/10.53106/199115992022103305001.

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<p>The K-means clustering algorithm is widely used in many areas for its high efficiency. However, the performance of the traditional K-means algorithm is very sensitive to the selection of initial clustering centers. Furthermore, except the convex distributed datasets, the traditional K-means algorithm still cannot optimally process many non-convex distributed datasets and datasets with outliers. To this end, this paper proposes the DP-Kmeans, an improved K-means algorithm based on the Density Parameter and center replacement, which can be more accurate than the traditional K-means by d

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Zhao, Huiling. "Design and Implementation of an Improved K-Means Clustering Algorithm." Mobile Information Systems 2022 (September 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/6041484.

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Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the ave

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Waode, Yully Sofyah, Anang Kurnia, and Yenni Angraini. "K-Means Optimization Algorithm to Improve Cluster Quality on Sparse Data." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 3 (2024): 641–52. http://dx.doi.org/10.30812/matrik.v23i3.3936.

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The aim of this research is clustering sparse data using various K-Means optimization algorithms. Sparse data used in this research came from Citampi Stories game reviews on Google Play Store. This research method are Density Based Spatial Clustering of Applications with Noise-Kmeans (DB-Kmeans), Particle Swarm Optimization-Kmeans (PSO-Kmeans), and Robust Sparse Kmeans Clustering (RSKC) which are evaluated using the silhouette score. Clustering sparse data presented a challenge as it could complicate the analysis process, leading to suboptimal or non-representative results. To address this cha

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Karthika, N., and B. Janet. "Feature Pair Index Graph for Clustering." Journal of Intelligent Systems 29, no. 1 (2019): 1179–87. http://dx.doi.org/10.1515/jisys-2018-0338.

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Abstract Text documents are significant arrangements of various words, while images are significant arrangements of various pixels/features. In addition, text and image data share a similar semantic structural pattern. With reference to this research, the feature pair is defined as a pair of adjacent image features. The innovative feature pair index graph (FPIG) is constructed from the unique feature pair selected, which is constructed using an inverted index structure. The constructed FPIG is helpful in clustering, classifying and retrieving the image data. The proposed FPIG method is validat

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Trushali, Jambudi, and Gandhi Savita. "An Effective Initialization Method Based on Quartiles for the K-means Algorithm." Indian Journal of Science and Technology 15, no. 35 (2022): 1712–21. https://doi.org/10.17485/IJST/v15i35.714.

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Abstract <strong>Objectives:</strong> This study aims to speed up the K-means algorithm by offering a deterministic quartile-based seeding strategy for initializing preliminary cluster centers for the K-means algorithm, enabling it to efficiently build high-quality clusters. <strong>Methods:</strong> We have investigated various cluster center initialization approaches in literature and presented our findings. For the Kmeans algorithm, we here propose a novel deterministic technique based on quartiles for finding initial cluster centers. To obtain the preliminary cluster centers

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Hao, Zheng. "Railway Passenger Customer Segmentation Method Based on User Preferences." Advanced Materials Research 850-851 (December 2013): 1028–31. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.1028.

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This paper uses the clustering model SOM-Kmeans two segment to cluster the users on railway ticket selling system. First, it describes the types of customer segmentation and the general segmentation steps, then introduces the definition of user preferences, at last on the basis of calculation steps of SOM-Kmeans two segment algorithm, the customer segmentation model and algorithm is given based on user preferences.

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Y., Hamzaoui, Amnai M., Choukri A., and Fakhri Y. "Enhancenig OLSR routing protocol using K-means clustering in MANETs." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3715–24. https://doi.org/10.11591/ijece.v10i4.pp3715-3724.

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The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method t

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Hamzaoui, Y., M. Amnai, A. Choukri, and Y. Fakhri. "Enhancenig OLSR routing protocol using K-means clustering in MANETs." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 3715. http://dx.doi.org/10.11591/ijece.v10i4.pp3715-3724.

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The design of robust routing protocol schemes for MANETs is quite complex, due to the characteristics and structural constraints of this network. A numerous variety of protocol schemes have been proposed in literature. Most of them are based on traditional method of routing, which doesn’t guarantee basic levels of Qos, when the network becomes larger, denser and dynamic. To solve this problem we use one of the most popular methods named clustering. In this work we try to improve the Qos in MANETs. We propose an algorithm of clustering based in the new mobility metric and K-Means method to dist

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YIN, JIAN, SHAOE XUE, JINA WANG, SHUQIN RAO, and WENXIN YANG. "PRESERVING LOCAL MANIFOLD IN NOISE DATA CLUSTERING." Journal of Circuits, Systems and Computers 18, no. 08 (2009): 1547–63. http://dx.doi.org/10.1142/s021812660900585x.

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Adaptive subspace clustering techniques, such as LDA Kmeans (henceforth LDAKM), try to perform clustering on a compact discriminative subspace. However, the subspace extracted by LDAKM may be inferior when encountering noise, so it is with the clustering results. In this paper, we generalize the LDAKM algorithm, and propose the Local Manifold Preserving LDAKM (henceforth LMP–LDAKM) approach, which considers both local manifold structure and discriminative information. A Laplacian similarity matrix is introduced in the subspace extraction subprocess for preserving local manifold information whi

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Deepak T. Mane, Pradeep Kundlik Deshmukh. "Efficient Training of Colorectal Cancer Diagnosis Model through Unsupervised Learning Composite Network." Journal of Electrical Systems 20, no. 1s (2024): 114–25. http://dx.doi.org/10.52783/jes.757.

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The use of machine learning algorithms for precise and effective colorectal cancer diagnosis has gained popularity as a result of advancements in medical imaging. Through the use of an Unsupervised Learning Composite Network (ULCN) model this study suggests a novel method for training a diagnosis model. Conventional training techniques for these models frequently rely on sizable labelled datasets, which can be labour and resource-intensive to create. The ULCN, on the other hand, incorporates unsupervised learning into the training process, decreasing the need on labelled input. In order to add

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Arwani, Issa. "Optimasi Proses Klasterisasi di MySQL DBMS dengan Mengintegrasikan Algoritme MIC-Kmeans Menggunakan Bahasa SQL dalam Stored Procedure." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 2 (2020): 391. http://dx.doi.org/10.25126/jtiik.2020702639.

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<p>Proses klasterisasi data di <em>DBMS</em> akan lebih efisien jika dilakukan langsung di dalam <em>DBMS</em> itu sendiri karena <em>DBMS</em> mendukung untuk pengelolaan data yang baik. <em>SQL-Kmeans</em> merupakan salah satu metode yang sebelumnya telah digunakan untuk mengintegrasikan algoritme klasterisasi <em>K-means</em> ke dalam <em>DBMS</em> menggunakan <em>SQL</em>. Akan tetapi, metode ini juga membawa kelemahan dari algoritme <em>K-means</em> itu sendiri yaitu lamanya iterasi untuk

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Silva-Blancas, Victor Hugo, Hugo Jiménez-Hernández, Ana Marcela Herrera-Navarro, José M. Álvarez-Alvarado, Diana Margarita Córdova-Esparza, and Juvenal Rodríguez-Reséndiz. "A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling." Computers 13, no. 6 (2024): 149. http://dx.doi.org/10.3390/computers13060149.

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With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer percept

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Sandosh, S., Akila Bala, and Nithin Kodipyaka. "Z-K-R: A Novel Framework in Intrusion Detection system through enhanced techniques." Journal of Information Assurance and Security 19, no. 2 (2024): 56–71. http://dx.doi.org/10.2478/ias-2024-0005.

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Abstract Intrusion detection systems (IDS) are an important tool for securing computer networks from various types of cyberattacks. The increasing complexity of network attacks demands more sophisticated approaches to intrusion detection. This paper presents an innovative method for IDS that involves combining Z-Score outlier detection, KMeans clustering, and Random Forest classification techniques. We tested our methodology using the CICIDS2017 dataset, which is a standardization dataset for intrusion detection that is frequently utilized. Our proposed approach first uses Z-Score outlier dete

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Feitelberg, Jacob, Tamás Budavári, and Amitabh Basu. "Fast Catalog Matching for Improved Posteriors via Constrained Clustering." Astronomical Journal 169, no. 4 (2025): 210. https://doi.org/10.3847/1538-3881/adba4d.

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Abstract We propose a novel approximate method for the probabilistic catalog matching problem that provides better solutions than previously used heuristics and scales well for large real-world applications. We also improve probabilistic catalog matching by including a simple but powerful prior and optimizing the posterior instead of just the likelihood as in previous formulations. Our new approach uses constrained clustering, specifically COP-KMeans, to provide near-optimal solutions in a fraction of the time of previous methods. We empirically demonstrate our constrained clustering’s efficac

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Chen, Hao-xuan, Fei Tao, Pei-long Ma, Li-na Gao, and Tong Zhou. "Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory." ISPRS International Journal of Geo-Information 10, no. 3 (2021): 161. http://dx.doi.org/10.3390/ijgi10030161.

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Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estima

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LI, Chunlei, Zhiyong CHANG, and Liang LI. "A novel system for discovery and reuse of typical process route based on information entropy and PSO-Kmeans clustering algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 41, no. 1 (2023): 198–208. http://dx.doi.org/10.1051/jnwpu/20234110198.

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Manufacturing enterprises will accumulate a large number of manufacturing instances as they run and develop. Being able to excavate and reuse the instance resources reasonably is one of the most effective ways to improve manufacturing and support innovation. To determine the reuse object scientifically and raise the reuse flexibility, a novel system for discovery and reuse of typical process route based on the information entropy and PSO-Kmeans clustering algorithm is proposed in this paper. In this system, a similarity measurement method of machining process routes based on the information en

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Maulana, Didit Johar, Siti Saadah, and Prasti Eko Yunanto. "Kmeans-SMOTE Integration for Handling Imbalance Data in Classifying Financial Distress Companies using SVM and Naïve Bayes." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 1 (2024): 54–61. http://dx.doi.org/10.29207/resti.v8i1.5140.

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Imbalanced data presents significant challenges in machine learning, leading to biased classification outcomes that favor the majority class. This issue is especially pronounced in the classification of financial distress, where data imbalance is common due to the scarcity of such instances in real-world datasets. This study aims to mitigate data imbalance in financial distress companies using the Kmeans-SMOTE method by combining Kmeans clustering and the synthetic minority oversampling technique (SMOTE). Various classification approaches, including Nave Bayes and support vector machine (SVM),

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Rohma, Fatona Fadilla, Iklima Ermis Ismail, and Yoyok Sabar Waluyo. "Implementation Of Kmeans Clustering On SIPP-KLING Dashboard Applications." MULTINETICS 4, no. 2 (2018): 38–42. http://dx.doi.org/10.32722/multinetics.v4i2.1336.

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This study focused on classifying rumah_sehat data into five categories, namely Healthy, Very Healthy, Unhealthy, Unhealthy, Very Unhealthy. The criteria that will be the input parameters for K-Means calculation are 17 criteria. The implementation of the K-Means Clustering will help in classifying healthier homes that are more filtered, based on 8969 data. Data obtained from the results of clustering k-means can help analyze what parts of a house should be handled more, or which areas have lower levels of health. The test results show that from 8969 data, there were 3303 Very Healthy homes, 24

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Rohma, Fatona Fadilla, Iklima Ermis Ismail, and Yoyok Sabar Waluyo. "Implementation Of Kmeans Clustering On SIPP-KLING Dashboard Applications." MULTINETICS 4, no. 2 (2018): 38–42. http://dx.doi.org/10.32722/multinetics.vol4.no.2.2018.pp.38-42.

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This study focused on classifying rumah_sehat data into five categories, namely Healthy, Very Healthy, Unhealthy, Unhealthy, Very Unhealthy. The criteria that will be the input parameters for K-Means calculation are 17 criteria. The implementation of the K-Means Clustering will help in classifying healthier homes that are more filtered, based on 8969 data. Data obtained from the results of clustering k-means can help analyze what parts of a house should be handled more, or which areas have lower levels of health. The test results show that from 8969 data, there were 3303 Very Healthy homes, 24

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Jader, Rasool, and Sadegh Aminifar. "Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach." Applied Computational Intelligence and Soft Computing 2022 (October 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/9749579.

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Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes. The dataset was obtained from the Kurdist

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Huo, Yonghua, Yi Cao, Zhihao Wang, Yu Yan, Zhongdi Ge, and Yang Yang. "Traffic Anomaly Detection Method Based on Improved GRU and EFMS-Kmeans Clustering." Computer Modeling in Engineering & Sciences 126, no. 3 (2021): 1053–91. http://dx.doi.org/10.32604/cmes.2021.013045.

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Adnan, Rana Muhammad, Kulwinder Singh Parmar, Salim Heddam, Shamsuddin Shahid, and Ozgur Kisi. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering." Sustainability 13, no. 9 (2021): 4648. http://dx.doi.org/10.3390/su13094648.

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The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comp

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Zhang, Chuansheng, and Minglai Yang. "Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model." Applied Sciences 15, no. 7 (2025): 3530. https://doi.org/10.3390/app15073530.

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This study aims to improve the prediction accuracy of reference evapotranspiration under limited meteorological factors. Based on the commonly recommended PSO-ELM model for ET0 prediction and addressing its limitations, an improved QPSO algorithm and multiple kernel functions are introduced. Additionally, a novel evapotranspiration prediction model, Kmeans-QPSO-MKELM, is proposed, incorporating K-means clustering to estimate the daily evapotranspiration in Yancheng, Jiangsu Province, China. In the input selection process, based on the variance and correlation coefficients of various meteorolog

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Milleana Shaharudin, Shazlyn, Shuhaida Ismail, Siti Mariana Che Mat Nor, and Norhaiza Ahmad. "An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (2019): 237. http://dx.doi.org/10.11591/ijai.v8.i3.pp237-243.

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<p>In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and bi-clustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhib

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P, Ravi, Naveena C, and H. Sharathkumar Y. "OCR for historical Kannada documents using clustering methods." Indian Journal of Science and Technology 13, no. 35 (2020): 3652–63. https://doi.org/10.17485/IJST/v13i35.1287.

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Abstract <strong>Motivation:</strong> In India, the Language Kannada is an ancient and official language in Karnataka State. The study of ancient Kannada scripts from stone carvings, leaf, metal, cloth, paper and other sources enhances our knowledge on the traditions and culture practiced in Karnataka. Due to Poor Quality, variability and the contrast, the Kannada ancient scripts become very challenging to extract the information or to recognize the characters. <strong>Objectives:</strong> To design a suitable Optical Character Recognition (OCR) technique to read ancient Kannada

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Lin, Yong, Yang Fan, Lie Xin Wu, Yan Hui Xu, and Ge Song. "Comparison Study of the Clustering Analysis Methods in the Load Time-Variation Research." Applied Mechanics and Materials 548-549 (April 2014): 1135–38. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1135.

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Load model has a great impact on the digital simulation result. In this paper, the measurement-based method is applied to model the load. If all the measured data are used for modeling respectively, the workload would be increased greatly. But if only one model is generated with the multi-curve fitting parameter identification method, the accuracy of modeling would be reduced greatly. The clustering analysis theory supplies an effective way to solve the problem above. There are some methods for clustering introduced in this paper. But a suitable method needs be studied firstly. The case study

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Shulenin, N. S., and R. N. Lemeshkin. "Application of the KMeans method for clustering the motives of terrorist attacks: a study of data density and distribution." Medicо-Biological and Socio-Psychological Problems of Safety in Emergency Situations, no. 2 (June 11, 2025): 108–20. https://doi.org/10.25016/2541-7487-2025-0-2-108-120.

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Relevance. Terrorism represents a significant threat to international security, with the diversity and complexity of terrorist attack motives complicating comprehensive analysis. The application of machine learning methods, particularly clustering, allows to identify hidden patterns and trends, providing deeper insights into the underlying causes and preconditions of terrorism.Objective. The study aims to apply the KMeans method to cluster terrorist attack motives, assess the density distribution of data within the identified clusters, and reveal key patterns in the motivational structure of t

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Prasetyo, Almas Nurfarid Budi, Maimunah Maimunah, and Pristi Sukmasetya. "K-Means Clustering Method for Determining Waste Transportation Routes to Landfill." Jurnal Riset Informatika 5, no. 3 (2023): 547–56. http://dx.doi.org/10.34288/jri.v5i3.540.

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Waste is worsening in Magelang City, especially in urban areas. As a result of poorly managed waste disposal, a landfill is needed. Magelang City has a landfill called TPA Banyuurip, located in Plumbon Hamlet, Banyuurip Village, Tegalrejo Subdistrict, Magelang City. From this case, the application of the kmeans clustering method to determine the efficiency of the waste transportation route to the landfill is needed. The research began by conducting direct observations at the Banyuurip landfill by interviewing the drivers of waste vehicles to find out information such as waste sources, transpor

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Prasetyo, Almas Nurfarid Budi, Maimunah Maimunah, and Pristi Sukmasetya. "K-Means Clustering Method for Determining Waste Transportation Routes to Landfill." Jurnal Riset Informatika 5, no. 3 (2023): 277–84. http://dx.doi.org/10.34288/jri.v5i3.219.

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Waste is worsening in Magelang City, especially in urban areas. As a result of poorly managed waste disposal, a landfill is needed. Magelang City has a landfill called TPA Banyuurip, located in Plumbon Hamlet, Banyuurip Village, Tegalrejo Subdistrict, Magelang City. From this case, the application of the kmeans clustering method to determine the efficiency of the waste transportation route to the landfill is needed. The research began by conducting direct observations at the Banyuurip landfill by interviewing the drivers of waste vehicles to find out information such as waste sources, transpor

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Alqadi, Ziad, and Mohammad Sleman Khrisat. "Analysis of Methods used to Create Digital Signals Fingerprints." International Journal of Computer Science and Mobile Computing 12, no. 8 (2023): 84–97. http://dx.doi.org/10.47760/ijcsmc.2023.v12i08.011.

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Digital signals are used in many vital applications, and since the volume of this data is very large, it is necessary to represent this data with a set of values and in a small size in order to increase the speed of processing the digital signal, because the applications that use this data need a high execution speed. In this paper research a detailed study of kmeans clustering and wavelet packet tree decomposition will be introduced, Using these methods to process any type of digital signals will lead to represent the digital data by a set of small number of values, this set will be used as a

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Okabe, Masayuki, and Seiji Yamada. "Active Sampling for Constrained Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 2 (2014): 232–38. http://dx.doi.org/10.20965/jaciii.2014.p0232.

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Constrained clustering is a framework for improving clustering performance by using constraints about data pairs. Since performance of constrained clustering depends on the set of constraints used, a method is needed to select good constraints that promote clustering performance. In this paper, we propose an active sampling method working with a constrained cluster ensemble algorithm that aggregates clustering results that a modified COP-Kmeans iteratively produces by changing the priorities of constraints. Our method follows the approach of uncertainty sampling and measures uncertainty using

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Ma, G. Q., Y. C. Tian, X. L. Li, K. Z. Xing, and Su Xu. "Color Image Segmentation of Live Grouper Fish with Complex Background in Seawater." Applied Mechanics and Materials 743 (March 2015): 293–302. http://dx.doi.org/10.4028/www.scientific.net/amm.743.293.

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The color live fish image segmentation is a important procedure of the understanding fish behavior. We have introduced an simple segmentation method of live Grouper Fish color images with seawater background and presented a segmentation framework to extract the whole fish image from the complex background of seawater. Firstly, we took true color pictures of live Grouper fish in seawater using waterproof camera and save these pictures files as RGB format files, called True-color Images. Secondly, we extracted R,G and B planes of a true color Grouper fish image, painted and compared their histog

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Shestakov, Kirill S., and Oleg N. Krakhmalev. "CLUSTERING APPROACHES FOR FINANCIAL DATA ANALYSIS." SOFT MEASUREMENTS AND COMPUTING 1, no. 62 (2023): 64–72. http://dx.doi.org/10.36871/2618-9976.2023.01.006.

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The company's financial statements are a direct reflection of its operations. It consists of several documents and has a strict structure, which allows specialists to identify the main problems and difficulties that the company faces with. Despite the existing base of methods of primary reporting analysis, there is no methodology for determining abnormal financial behavior on the part of industry representatives. In this article, the authors propose to use data mining methods to analyze various anomalies in accounting statements that would characterize one or another way of disposing of capita

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Xiang, P. S. "A Cloud Detection Algorithm for MODIS Images Combining Kmeans Clustering and Otsu Method." IOP Conference Series: Materials Science and Engineering 392, no. 6 (2018): 062199. http://dx.doi.org/10.1088/1757-899x/392/6/062199.

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Anh Phong, PHAN, and LE Van Thanh. "IMPROVING PERFORMANCE FOR IMBALANCED DATA CLASSIFICATION USING OVERSAMPLING AND CHARACTERISTICS OF EACH CLUSTER." Vinh University Journal of Science 53, no. 3A (2024): 5–15. http://dx.doi.org/10.56824/vujs.2024a054a.

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This paper proposes a method to enhance the effectiveness of classifying imbalanced data. The main contribution of the method is integrating the K-means clustering algorithm and the minority oversampling technique VCIR to generate synthetic samples that closely represent the actual data characteristics. Experimental results have shown that the proposed method performs better on several metrics than current popular methods for handling imbalanced data, such as SMOTE, Borderline-SMOTE, Kmeans-SMOTE, and SVM-SMOTE. Keywords: Data classification; imbalanced data; oversampling; K-Means; SMOTE.

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Wang, Xiaoni. "The Research of the Distributed Resource-AwareK-means Clustering Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 3 (2015): 343–48. http://dx.doi.org/10.20965/jaciii.2015.p0343.

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According to the characteristics of the constrained resource in distributed real-time data mining in the Internet of Things (IOT) environment, a distributed data mining method is researched in such environment. Based on the limits of computing ability, storage ability, battery energy resources, network bandwidth, and the Internet single point failure, the distributed network data mining method is researched, and the adaptive technology and peer-to-peer node method are adopted. The DRA-Kmeans algorithm of data mining based on theK-means algorithm is proposed, and the amount of data communicatio

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Danganan, Alvincent E., and Regina P. Arceo. "Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (2022): 1607–15. https://doi.org/10.11591/ijeecs.v26.i3.pp1607-1615.

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Most natural world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses kmedian to identify

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LI, H. X., SHITONG WANG, and YU XIU. "APPLYING ROBUST DIRECTIONAL SIMILARITY BASED CLUSTERING APPROACH RDSC TO CLASSIFICATION OF GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 04, no. 03 (2006): 745–68. http://dx.doi.org/10.1142/s0219720006002144.

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Despite the fact that the classification of gene expression data from a cDNA microarrays has been extensively studied, nowadays a robust clustering method, which can estimate an appropriate number of clusters and be insensitive to its initialization has not yet been developed. In this work, a novel Robust Clustering approach, RDSC, based on the new Directional Similarity measure is presented. This new approach RDSC, which integrates the Directional Similarity based Clustering Algorithm, DSC, with the Agglomerative Hierarchical Clustering Algorithm, AHC, exhibits its robustness to initializatio

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Safitri, Emeylia, I. Made Sumertajaya, and Akbar Rizki. "Penggerombolan Provinsi di Indonesia Berdasarkan Produktivitas Tanaman Pangan Tahun 2005-2015 Menggunakan Metode K-Error." Xplore: Journal of Statistics 2, no. 1 (2018): 25–32. http://dx.doi.org/10.29244/xplore.v2i1.75.

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Clustering analysis is a multivariate analysis that’s aim for gruping the observasion objects to some groups. The clusters have low similarity between the clusters and high similarity in same cluster. Classic grouping analysis have a weakness that doesn’t insert measurement error information that related with data. Clustering analysis with K-Error method is expanded for solusing solving the measurement error data problem in classic grouping analysis. The research is aim for clustering the provinces in Indonesia using K-Error and K-Means method based on crops productivity. K-Error method produc

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Abdulnassar, A. A., and Latha R. Nair. "A Comprehensive Study on the Importance of the Elbow and the Silhouette Metrics in Cluster Count Prediction for Partition Cluster Models." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 3792–806. http://dx.doi.org/10.47059/revistageintec.v11i4.2408.

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Proper selection of cluster count gives better clustering results in partition models. Partition clustering methods are very simple as well as efficient. Kmeans and its modified versions are very efficient cluster models and the results are very sensitive to the chosen K value. The partition clustering algorithms are more suitable in applications where the data are arranged in a uniform manner. This work aims to evaluate the importance of assigning cluster count value in order to improve the efficiency of partition clustering algorithms using two well known statistical methods, the Elbow metho

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Jabbar, Jabbarmuhammad. "SISTEM INFORMASI STOK BARANG MENGGUNAKAN METODE CLUSTERING KMEANS (STUDI KASUS RMD STORE)." INFOTECH journal 8, no. 1 (2022): 70–75. http://dx.doi.org/10.31949/infotech.v8i1.2280.

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This inventory information system was created to obtain information on goods that were sold in large quantities and those that were not sold (less desirable) in order to reduce the buildup of unsold goods and minimize losses caused by goods that were not in demand, therefore we made an application of the inventory information system using the method clustering KMeans case study RMD Store using PHP Laravel and MySqli.

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Wang, Ning, Wenqing Zhu, Huiying Fang, and Weimin Zhao. "An Empirical Study on User Role Discovery Based on Clustering Algorithms and Optimizations in Location-Based Social Network." Journal of Internet Technology 25, no. 6 (2024): 887–98. http://dx.doi.org/10.70003/160792642024112506009.

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Location-Based Social Network (LBSN) has been widely used in social lives. Role is an important concept in user’s personalized analysis. Many automatic methods such as machine learning method and social network analysis method have been used in user role discovery in LBSN, however, the effectiveness of these methods has not been comprehensively analyzed. In this paper, firstly, the effectiveness of five clustering algorithms is comprehensively analyzed, including K-means algorithm, Bi-Kmeans algorithm, DBSCAN (Density-Based Spatial Clustering Application with Noise) algorithm, OPTICS (Ordering

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Liu, Lijun, Xiaoyang Wu, Jun Yu, Yuduo Zhang, Kaixing Niu, and Anli Yu. "scVGATAE: A Variational Graph Attentional Autoencoder Model for Clustering Single-Cell RNA-seq Data." Biology 13, no. 9 (2024): 713. http://dx.doi.org/10.3390/biology13090713.

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Single-cell RNA sequencing (scRNA-seq) is now a successful technology for identifying cell heterogeneity, revealing new cell subpopulations, and predicting developmental trajectories. A crucial component in scRNA-seq is the precise identification of cell subsets. Although many unsupervised clustering methods have been developed for clustering cell subpopulations, the performance of these methods is prone to be affected by dropout, high dimensionality, and technical noise. Additionally, most existing methods are time-consuming and fail to fully consider the potential correlations between cells.

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Risnasari, Medika, Nabila Aulia, and Laili Cahyani. "Clustering Of Student Learning Styles in the industri 4.0 Using KMeans Algorithm." JTP - Jurnal Teknologi Pendidikan 24, no. 2 (2022): 246–57. http://dx.doi.org/10.21009/jtp.v24i2.28029.

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Clustering is a technique for grouping homogeneous data so that the points in each cluster are as similar as possible according to convenience measures such as Euclidean-based distance or correlation-based distance. In the industrial era 4.0, learning media, the environment, the way teachers teach will affect student learning styles. From research on learning styles, many researchers agree on the importance of identifying learning styles to accelerate their learning performance. The purpose of this study is to classify student learning styles in the industrial era 4.0 using the Kmeans algorith

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Pal, Samyajoy, and Christian Heumann. "Clustering compositional data using Dirichlet mixture model." PLOS ONE 17, no. 5 (2022): e0268438. http://dx.doi.org/10.1371/journal.pone.0268438.

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A model-based clustering method for compositional data is explored in this article. Most methods for compositional data analysis require some kind of transformation. The proposed method builds a mixture model using Dirichlet distribution which works with the unit sum constraint. The mixture model uses a hard EM algorithm with some modification to overcome the problem of fast convergence with empty clusters. This work includes a rigorous simulation study to evaluate the performance of the proposed method over varied dimensions, number of clusters, and overlap. The performance of the model is al

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C, Nithyasree, Stanley D, and Subalakshmi K. "Brain Tumor Detection using Image Processing." International Journal on Cybernetics & Informatics 10, no. 2 (2021): 319–25. http://dx.doi.org/10.5121/ijci.2021.100235.

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Brain tumor extraction and its analysis are challenging tasks in medical image processing because brain image is complicated .Segmentation plays a very important role in the medical image processing.In that way MRI (magnetic resonance imaging )has become a useful medical diagnostic tool or the diagnosis o brain & other medical images.In this project, we are presenting a comparative study of three segmentation methods implemented or tumor detection .The method includes kmeans clustering using watershed algorithm . Optimized k-means and optimized c-means using genetic algorithm.

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