K-means Clustering Method Research Articles

Metabolic Status Modulates Global and Local Brain Age Estimates in Overweight and Obese Adults

BackgroundAs people live longer, maintaining brain health becomes essential for extending healthspan and preserving independence. Brain degeneration and cognitive decline are major contributors to disability. This study investigates how metabolic health influences brain-age-gap-estimate (brainAGE), which measures the difference between neuroimaging-predicted brain age and chronological age. MethodsK-means clustering was applied to fasting metabolic markers including insulin, glucose, leptin, cortisol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, steady-state-plasma glucose and of body mass index of 114 physically and cognitively healthy adults. The Homeostatic Model Assessment for Insulin Resistance served as a reference. T1-weighted brain MRIs were used to calculate voxel-level and global (G-brainAGE). Longitudinal data were available for 53 participants over a 3-year interval. ResultsK-mean clustering divided the sample into two groups: those with favorable (N=56) and suboptimal metabolic health (N=58). The suboptimal group showed signs of insulin resistance and dyslipidemia (PFDR<0.05) and had older G-brainAGE and L-brainAGE, with deviations most prominent in cerebellar, ventromedial prefrontal, and medial temporal regions (PFWE<0.05). Longitudinal analysis revealed group differences but no significant time or interaction effects on brainAGE measures. ConclusionsSuboptimal metabolic status is linked to accelerated brain aging, particularly in brain regions rich in insulin receptors. These findings highlight the importance of metabolic health in maintaining brain function and suggest that promoting metabolic well-being may help extend healthspan.

Research on Automatic Alignment for Corn Harvesting Based on Euclidean Clustering and K-Means Clustering

Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%.

Debiasing weighted multi-view k-means clustering based on causal regularization

In the field of unsupervised learning, many methods such as clustering rely on exploring the correlations among features. However, considering these correlations is not always advantageous for learning models. The biased selection of data may lead to redundant and unstable correlations among features, adversely affecting the performance of learning models. Multi-view data presents more complex feature correlations with potential redundancy and varying distributions across views, necessitating detailed analysis. This paper proposes a causal regularized debiased multi-view k-means clustering (DMKC) method to counteract redundant feature correlations stemming from sample selection bias. This method introduces a covariate weighted balance method from causal inference to mitigate redundant bias in multi-view clustering by adjusting sample weights. The approach combines sample and view weights within a k-means loss framework, effectively eliminating feature redundancy and enhancing clustering performance amidst sample selection bias. The optimization process of the relevant parameters is detailed in this paper, and comprehensive experiments demonstrate the effectiveness of the method.

Implementation of K-Means Clustering in Mapping Teacher Distribution Using Geographic Information System

The placement of teachers in Indonesia has not been evenly distributed across several regions due to inaccurate recruitment and placement processes. The quality of education, particularly in rural areas, is negatively impacted by this uneven distribution. Teachers play a crucial role in enhancing education, making it essential to address this issue. This study seeks to equilibrate the allocation of teachers in Langsa City using the K-Means Clustering method based on the number of teachers, students, and study groups at the Madrasah Ibtidaiyah, Madrasah Tsanawiyah, and Madrasah Aliyah levels. The clustering results are then mapped using the Quantum Geographic Information System. The study identifies 20 schools with a shortage of teachers, 7 schools with sufficient teachers, and 3 schools with a surplus. The utilization of the K-Means Clustering method demonstrated a high accuracy rate of 92.8%. The implications of these findings suggest that educational authorities can use the clustering results to strategically address teacher shortages by reallocating teaching resources more effectively, thus potentially improving educational outcomes in underserved areas. Moreover, the GIS mapping offers a practical tool for ongoing monitoring and decision-making regarding teacher distribution.

Implementation of the Web-Based K-Means Clustering Algorithm on Hypertension Levels in the Elderly at the Bungah District Health Center

This research was carried out at the community health center in Bungah sub - district with the aim that so that patients receive appropriate treatment, a web-based system is needed to group hypertension data into several classes (clusters). The method used in data grouping is the K-Means Clustering method . In this study, 100 hypertension data were grouped into 4 clusters . The hypertension data used consists of two attributes, namely systole and diastole, the working mechanism is to normalize the data first, then the system groups the data into groups that have the same characteristics. The system will display the results of the clustering process which consists of four (4) clusters , namely cluster 1 Isolated Systolic Hypertension, cluster 2 Grade 1 (mild hypertension), cluster 3 Grade 2 (moderate hypertension), and cluster 4 Grade 3 (high hypertension). The data used for clustering is 100 data from blood pressure checks of patients at the Community Health Center using a blood pressure monitor. The results of the last iteration of 100 hypertension data in the system were used as parameters for calculating the level of effectiveness, by comparing the data from the clustering test results to the data from diagnosis a which produced an effectiveness value of 80%.

Development and safety evaluation of an adaptive personalized speed guidance system for on-ramp merging in highway service areas

As autonomous driving and Vehicle-to-Everything (V2X) technologies evolve, the efficiency and safety of ramp merging in the highway service areas have become increasing critical. This study introduces a closed-loop feedback speed guidance system that accommodates individual driving styles, aiming to optimize merging behaviour, reduce traffic accidents, and enhance total traffic efficiency. The system dynamically adjusts the merging vehicle speeds by continuously monitoring their speed and location with variable steps to promote smoother merging. Moreover, this research also involves collecting naturalistic driving data from ramp merging scenarios, using the K-means clustering and point estimation method to recognize and analyse driving style characteristics, and integrating these styles into the developed closed-loop feedback speed guidance system. This approach results in personalized speed guidance curves tailored to different driving styles, facilitating more efficient mering. Additionally, the study conducts a Safety of the Intended Functionality (SOTIF) evaluation of this system using the System-Theoretic Process Analysis (STPA) method, which helps identify potential security risks and develop appropriate mitigation strategies to ensure the system’s safe and stable operation. The simulation results confirm that this innovative dynamic speed guidance system substantially improves traffic safety and efficiency in ramp merging areas.

A Radial Distribution Function Based Recognition Algorithm of Point Defects in Large-Scale β-Ga2O3 Systems.

The atomic configurations and concentrations of intrinsic defects profoundly influence the electrical and optical properties of the semiconductor materials. This influence is particularly significant in the case of β-Ga2O3, which is a highly promising ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. We design an effective algorithm for the explicit identification of various intrinsic point defects in the β-Ga2O3 lattice, which constitutes the integration of the particle swarm optimization (PSO) and K-means clustering (K-MC) methods. Our algorithm attains the recognition accuracy exceeding 95%. Finally, the algorithm is applied to dynamic simulations, where the feasibility of dynamic real-time detection is explored.

Market segmentation analysis to find out products and services that suit customer needs using the python KMEANS clustering method (Case study: Superindo Tambun Area, Bekasi)

This study analyzes market segmentation of 1000 Superindo consumers in Tambun, Bekasi, using K-Means Clustering with Python 3.10. Variables included age, gender, occupation, purchase amount, product type, and discounts. Three main segments were identified: "Business Women" (average age 40, focus on staple goods, 16% discount), "Women Government Career" (age 39, staple goods, 16% discount), and "Professional Women" (age 38, staple goods, 15% discount). Superindo should target "Women Government Career" with the highest purchasing power. To increase sales, an online value proposition is recommended offering bundled packages of staple goods with meat and fruit.

Longitudinal Data Clustering Methods: A Systematic ReviewLongitudinal Data Clustering Methods: A Systematic Review

Introduction: In the last few decades, in many research fields, different methods were introduced to discover groups with the same trends in longitudinal data. The clustering process is an unsupervised learning method, which classifies longitudinal data based on different criteria by performing algorithms. The current study was performed with the aim of reviewing various methods of longitudinal data clustering, including two general categories of non-parametric methods and model-based methods. Methods: In this research, to obtain related scientific articles, PubMed, Science Direct Scopus, ISI, and Google Scholar were searched between 2000 and 2021. Results: According to our systematic review, the non-parametric k-means Clustering Method utilizing Euclidean distance emerges as a leading approach for clustering longitudinal data. Conclusion: This research, with an overview of the studies done in the field of clustering, can help researchers as a toolbox to choose various methods of longitudinal data clustering in idea generation and choosing the appropriate method in the classification and analysis of longitudinal data.

An observational study of female body shape characteristics in multiracial Malaysia

Abstract The current study aims to explore the representative and typical female body shapes through in-depth observation and analysis of the diversity in Malaysian female body shape characteristics across different ethnic and age groups, which assists in facilitating the establishment of accurate apparel size standards. This study was conducted in Selangor, which is the most populous and developed Malaysian state. The data were collected via a highly systematic approach. The K-mean clustering method was applied to synthesise and analyse the collected data, which classified the body types into five major clusters. The results not only revealed significant differences in the distribution of body shapes across ethnic and age groups but also highlighted the limitations of the various sizing systems currently employed by the Malaysian apparel industry to fulfil the requirements of the diverse Malaysian market. The findings provided practical implications for apparel manufacturers and retailers with concrete evidence to support improved product design and enhance customer satisfaction. A deeper understanding and categorisation of Malaysian females’ body types could also assist the apparel industry in developing more tailored sizing systems. Resultantly, an improved system minimises return rates, optimises inventory management, and supports environmental and sustainability goals by reducing overproduction.

Extensive Exploration of The Preference Correlations Between Architectural Features and Musical Attributes

This study investigates the correlations between empirically-based preferences for architectural features and self-reported preferences for musical attributes. Altogether, 16 architectural attributes related to architectural complexity, indentation, symmetry, rhythm, pattern, and stress and 36 musical attributes of genres, perceived psychological attributes, Five Factors of Music, and 3-Factors are considered in the study, and their correlations are examined across 61 different demographic classes. In short, after gathering the data, Pearson’s correlation coefficient analysis examined the existence of correlation, and Bonferroni correction screened the most reliable ones. Then, categorization and K-mean clustering methods summarized the outcomes to provide a holistic understanding of the discovered correlations. The former reflected a higher number of correlations within genre and FFM in music, and indentation, complexity, and symmetry in architecture. The latter presented interesting correlation trends, including between complicated music and complicated architecture, rap music and the existence of pattern in architecture, and mellow music and simple architectural forms.

Implementation Machine Learning of K-Means Clustering Method and Linear Regression for Detecting the Risk of Tuberculosis Spread in Bangka Regency

The number of tuberculosis (TB) sufferers in Bangka Regency tends to increase and is becoming a very serious public health problem. In 2023, there will be around 331,581 residents of Bangka Regency, with around 1,489 of them suffering from TB. The high number of TB cases in Bangka Regency requires comprehensive monitoring. Surveillance can be carried out by grouping TB cases by region. One clustering method is K-Means which groups data based on similar criteria. Researchers also used the Linear Regression method to predict the effect of population density on increasing TB prevalence. This research was conducted to cluster the distribution pattern of TB by region and identify the influence of population density variables on the increase in TB. Based on the results of the K-Means method analysis by looking at the Performance Davies Bouldin-Index (Dbi) test, researchers obtained a pattern of TB distribution which was categorized into: Areas with High Prevalence (Sungailiat), Medium Prevalence (Belinyu and West Mendo) and Low Prevalence (Bakam, Merawang, Pemali, Pudding Besar, and Riau Silip). Meanwhile, the prediction results show that there is a positive influence of 81% of the population density variable on increasing the number of TB spreads and 19% is influenced by other variables. The results of this research can be used as a reference for the Bangka District Health Service to assist decision-making in preventing an increase in TB cases.

The Best Tourism Recommendation Intelligent System Model: Weighted Product and K-Means Methods

Magelang Regency, with a wealth of tourist destinations with a variety of things, such as Borobudur Temple and Nepal Van Java, has extraordinary tourism potential. With this diversity, it is a challenge for tourists to determine the best tourist objects that suit their preferences. This study aims to develop an intelligent system for recommending the selection of the best tourist attractions in Magelang Regency by integrating the Weighted Product and K-Means Clustering Methods. The system is designed to provide accurate recommendations based on tourist criteria such as location, facilities, tickets, and security, as well as group attractions based on their level of potential. The Weighted Product method is used to determine the best tourist attractions, while K-Means Clustering groups tourist destinations into high, medium, and low potential categories. In this study, several stages were carried out: literature study, data collection, system design, data analysis, implementation, and system testing to produce an effective and efficient recommendation system for tourists in Magelang Regency. The results of this research obtained the best tourism, namely at Borobudur Temple.

Optimization of K-Means Clustering Method for Drug Grouping at Mertoyudan I Health Center, Magelang

Health centers as health service providers have an important role in ensuring adequate drug availability for the community. However, drug management in health centers often faces obstacles, including the use of manual methods in stock monitoring, which can lead to unavailability or accumulation of unused drugs. This study aims to improve the efficiency of drug management in health centers through the application of the K-Means Clustering method. This method is used to group drug stocks into three main clusters: heavy drugs, moderate drugs, and light drugs. With this grouping, it is expected that pharmacists can more easily determine the type of drug, monitor drug availability in a timely manner, and manage drug procurement according to patient needs. Research methods include literature studies, data collection and analysis, system modeling design, and system testing and evaluation. The results of this study are expected to improve the efficiency of drug management in health centers, so that health services to the community can be more optimal. The results of testing using the Silhouette Coefficient (SC) method produced an average SC of 0.97 which means strong structure, so it can be concluded that it has a strong structure.

Graph-based analysis of DNA sequence comparison in closed cotton species: A generalized method to unveil genetic connections.

Graph theory provides a systematic method for modeling and analysing complicated biological data as an effective bioinformatics tool. Based on current trends, the number of DNA sequences in the DNA database is growing quickly. To determine the origin of a species and identify homologous sequences, it is crucial to detect similarities in DNA sequences. Alignment-free techniques are required for accurate measures of sequence similarity, which has been one of the main issues facing computational biologists. The current study provides a mathematical technique for comparing DNA sequences that are constructed in graph theory. The sequences of each DNA were divided into pairs of nucleotides, from which weighted loop digraphs and corresponding weighted vectors were computed. To check the sequence similarity, distance measures like Cosine, Correlation, and Jaccard were employed. To verify the method, DNA segments from the genomes of ten species of cotton were tested. Furthermore, to evaluate the efficacy of the proposed methodology, a K-means clustering method was performed. This study proposes a proof-of-model that utilises a distance matrix approach that promises impressive outcomes with future optimisations to be made to the suggested solution to get the hundred percent accurate result. In the realm of bioinformatics, this paper highlights the use of graph theory as an effective tool for biological data study and sequence comparison. It's expected that further optimization in the proposed solution can bring remarkable results, as this paper presents a proof-of-concept implementation for a given set of data using the proposed distance matrix technique.

A Machine Learning Approach to Analyzing Crime Concentration: The Case of New York City

Building upon prior work, we propose an alternative way to look at the pattern of spatial crime concentration and temporal stability of it. We first identify a high-crime cluster using the sample block groups in New York City by employing a k-means clustering method. We then examine the temporal stability of the high-crime cluster over time. We also longitudinally assess how our high-crime cluster classification is associated with the actual amount of crime while accounting for the measures of social and physical environments. We observed that about 6–12% of total areas are identified to be in the high-crime cluster. We also found that block groups identified to be high-crime cluster in one year are more likely to be that way in the next year. We hope future research may consider using data-driven approaches to expand understanding of spatial and temporal crime patterns.

Mapping the Wuling vehicle market with K-Means Clustering: An effective digital marketing strategy

This study focuses on Indonesia’s automotive industry sector, which is currently experiencing growth, particularly in terms of Wuling's contribution to the economy through sales. The aim is to identify customer clusters for Wuling vehicle and the marketing mix strategy after the most dominant customer cluster for Wuling vehicle. The research method used was a quantitative survey, which involved collecting data from 111 potential Wuling customer using purposive sampling and data collection through questionnaires. The analysis included an F-Test to examine the differences between clusters. The results show that the clustering of Wuling customer using the K-Means Clustering method successfully divided them into three different clusters, namely Perfectionist, Easy Going, and Beginner, with the Easy Going being the most dominant. Therefore, it is necessary to adjust marketing strategies to focus more on the needs and preferences of the Easy Going, including optimizing the use of promotion channels that have been proven effective, such as direct marketing and sales websites. Thus, this study emphasizes the importance of applying the K-Means Clustering method in automotive market segmentation, providing valuable insights for Wuling to formulate more effective and relevant marketing strategies to meet the diverse needs of customer in a dynamic market.

IMPLEMENTASI ALGORITMA CLUSTERING K-MEANS UNTUK PENGELOMPOKAN JENIS LAYANAN PENGGUNA JARINGAN

Advances in information technology are currently the most effective media for finding and disseminating the information needed in today's life. High information is sometimes not matched by the presentation of adequate information, often this information still needs to be extracted or reprocessed. As an intuition engaged in the field of communication and informatics, the Bogor Regency Communication and Informatics Service provides internet services for its employees. Therefore, the quality of the internet network at Diskominfo must always be in prime condition to support the process of its activities. For intuition, knowing and understanding the behavior of internet service users can be a reference for optimizing in the future. Access patterns are a part of user behavior when accessing internet services. Due to the large amount of data that must be processed, a method is needed to process such large data. In this thesis the author uses the K-Means Clustering method to process data. The results of this study indicate that the K-Means clustering algorithm is able to group types of network user services effectively based on the pattern of computer network usage in the Bogor District Communication and Informatics Office

A novel integrated method for improving the forecasting accuracy of crude oil: ESMD-CFastICA-BiLSTM-Attention

The volatility of crude oil price can significantly impact the stability of the crude oil market and even the global economy. Effectively predicting global crude oil price and volatility provides a scientific basis for decision-making for market regulators and investors worldwide, promoting the sound development of the global economy. In this study, we integrate signal processing with deep learning methods to present an optimal forecasting strategy for global crude oil price and volatility. We select the daily price of the WTI crude oil market from April 4, 1983, to December 12, 2023, for calculating volatility. Subsequently, employing extreme-point symmetric empirical mode decomposition (ESMD), K-means clustering, and fast independent component analysis method, we decompose and reconstruct the forecasting data, obtaining independent components with non-Gaussian characteristics. These components serve as inputs to estimate the accuracy of various models, including BiLSTM, Attention, LSTM, SVR, RF, and their combinations, in predicting crude oil price and volatility from both a point prediction and interval prediction perspective. Empirical results demonstrate that data decomposition, reconstruction, and the BiLSTM-Attention model outperform other models in predicting crude oil price and volatility.

Optimization of K-Means Clustering Method by Using Elbow Method in Predicting Blood Requirement of Pelamonia Hospital Makassar

Hospitals require an adequate supply of blood to meet patient needs. Accurate prediction of blood demand is essential to optimize inventory management and avoid shortages or overstocks. This study aims to predict blood demand at Pelamonia Hospital using K-Means Clustering and Elbow methods. Historical data on blood demand at Pelamonia Hospital was collected and processed. The Elbow method is used to determine the optimal number of clusters in the K-Means Clustering algorithm. Sum of Squared Errors (SSE) or Within-Cluster Sum of Squares (WCSS) values were calculated for various clusters, and the elbow point on the graph of SSE/WCSS vs. number of clusters was identified as the optimal number of clusters. Once the optimal number of clusters is determined, the K-Means Clustering algorithm is applied to the blood demand data, resulting in grouping the data into specific clusters. Each cluster is analyzed to find interesting patterns or characteristics, such as clusters with high or low blood demand. From the results of the SSE calculation process on 1057 blood demand data, the result that has the biggest decrease is at k = 4 with a difference value of 2754.90. The clustering results and patterns found are used to predict future blood demand by identifying which cluster best fits the current or expected conditions. The characteristics of the clusters are used to estimate the likely blood demand. This approach provides valuable insights into blood demand patterns and enables hospitals to better anticipate blood demand, thereby optimizing inventory management and improving the quality of healthcare services.