Non-hierarchical Cluster Analysis Research Articles

Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS

The challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues.

Urban tourists' receptivity to ecogamification: A technology, environment, and entertainment-based typology

This research explores the relationship between technology, entertainment, and the environment to understand urban tourists’ receptivity to ecogamified transport and mobility apps. Rather than providing conclusive solutions, this study follows an exploratory approach to establish relevant grounds for tourist clusters regarding their potential receptivity to ecogamification, an area that is still under explored. This study follows a segmentation approach, using a self-administered online questionnaire. The data, stemming from 572 adult respondents who have travelled to urban destinations in the past 3 years, were analysed through a combination of non-hierarchical and hierarchical cluster analyses. The results reveal four clusters of urban tourists with different types of potential receptivity to ecogamification: “Mobi Wholeheartedly”; “Mobi Whatever”; “Mobi Profiter”; and “Mobi Utilitarian”. This study adds to current research by adopting new segmentation approaches and more open assumptions concerning the potential receptivity to (eco)gamification. It also provides fresh evidence regarding tourist receptivity typologies and the existence of differences (and overlaps) among these types of tourists. It explores how that receptivity might occur based on the intersection between technology, entertainment and the environment. It examines the implications of tourist segments in ecogamification strategies, providing overall useful insights to destination management organizations, destination marketers and game designers.

Comparison of K-Means and Fuzzy C-Means Algorithms for Clustering Based on Happiness Index Components Across Provinces in Indonesia

Cluster analysis is a multivariate technique aimed at grouping objects into several clusters based on the characteristics they possess. This study aims to determine the clustering results of 34 provinces in Indonesia based on the indicators of the happiness index for the year 2021 by comparing non-hierarchical cluster analysis methods, namely K-Means and Fuzzy C-Means. K-Means is a non-hierarchical cluster analysis that divides objects into cluster groups based on the distance of objects to the nearest cluster center, while Fuzzy C-Means is a cluster analysis that uses a fuzzy grouping model where data becomes a member of a cluster formed based on membership degrees ranging from 0 to 1. Based on the research results, it is known that clustering with both K-Means and Fuzzy C-Means methods forms three clusters. Based on the standard deviation values between groups and the standard deviation ratio, the best method is the Fuzzy C-Means method because it has a larger standard deviation between groups and a smaller ratio compared to the K-Means method, which is 0.6680004. Therefore, this study concludes that the Fuzzy C-Means method is more optimal than the K-Means method.

Identifying intense inflammatory subtype of esophageal squamous cell carcinoma using clustering approach.

To establish a risk-stratification system for predicting the postoperative recurrence of esophageal squamous cell carcinoma, this study aimed to evaluate the prognostic value of clusters based on blood inflammation and coagulation markers and investigate their correlation with serum cytokines and genetic alteration. This single-center, retrospective cohort study enrolled 491 patients with esophageal cancer who underwent subtotal esophagectomy between 2004 and 2012. For cluster exploration, nonhierarchical cluster analysis and k-means were applied using serum C-reactive protein, albumin, fibrinogen, and platelet-lymphocyte ratio as variables. Then, multivariate survival analysis was conducted to investigate the association of clusters with recurrence-free survival. To characterize the clusters, serum interleukin-6, interleukin-8, and genetic alteration in primary tumors, the PleSSision-Rapid panel, which can evaluate 160 representative driver genes, was used. Patients were classified into clusters 1, 2, and 3, which included 24 (5%), 161 (33%), and 306 (62%) patients, respectively. Compared with cluster 3, cluster 1 or 2 had significantly worse recurrence-free survival. Based on the multivariable analysis using cluster, pStage, and age as covariates, cluster was an independent prognostic factor for recurrence-free survival (hazard ratio, 1.55; 95% confidence interval, 1.08-2.21; P = 0.02). The percentage of serum interleukin-6 and interleukin-8 levels was the highest in cluster 1, followed by clusters 2 and 3. In 23 patients with available genomic profiles, no significant difference in representative genomic alterations was observed. Non-biased clustering using inflammation and coagulation markers identified the intense inflammatory subtype, which had an independent prognostic effect on recurrence-free survival.

Multivariate Analysis to Study Genetic Diversity for Yield and its Attributing Traits in Rice (Oryza sativa L.)

The present investigation was carried out with 72 germplasm lines and three checks of rice (Oryza sativa L.) were grown at Crop Research Station, Masodha conducted in the GPB farm, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.) during Kharif June 2022- February 2023. Data on 10 characters, including grain yield per plant focused on diversity and PCA analysis. This investigation involved the analysis of 72 rice germplasm lines alongside three checks, showcasing extensive variation in agronomic and morphological traits. The study utilized Non-hierarchical Euclidean cluster analysis to assess genetic diversity. The pseudo F-test determined the optimal grouping of 75 genotypes into six distinct clusters. Cluster distribution revealed varying genotype compositions, with Cluster V comprising the highest entries (20), followed by Clusters I, VI, and II. Intra- and inter-cluster distances illustrated significant variability among clusters, emphasizing genetic diversity. Examining agronomic traits across these clusters revealed noteworthy variations in days to 50% flowering, days to maturity, plant height, and productive tillers. Panicle length, flag leaf area, biological yield, harvest index, 1000-grain weight, and grain yield per plant also exhibited cluster-specific variations. These findings provide valuable insights for rice breeding programs, facilitating targeted enhancements of specific agronomic traits within the rice population, thus contributing to the development of more resilient and productive rice varieties.

Semivariogram Modeling based on Provincial Clusters for Infectious Diseases and Mental Health in Indonesia

Health is a valuable asset that profoundly impacts individuals and society as a whole, enhancing overall well-being and quality of life. Both internal and external factors, along with geographical location, play a crucial role in health. These factors exhibit spatial patterns that can be effectively analyzed through geostatistical methods, particularly semivariogram modeling. This study explores appropriate semivariogram models to depict disease distribution in Indonesian provinces using data from National Health Insurance Agency (NHIA). The provinces will be grouped into five clusters based on the Consumer Price Index (CPI), health claim amounts, the number of participants, and 23 disease groups through non-hierarchical cluster analysis. Three clusters, with the most provinces, will be selected for semivariogram modeling: exponential, Gaussian, and Spherical models. The best-fitting semivariogram models are anisotropic exponential for claim amounts and anisotropic Gaussian for CPI, number of participants, infectious diseases, and mental health issues. Meanwhile, the most suitable spherical model is identified for a specific cluster (Kalimantan and Nusa Tenggara regions). The results of this modeling can serve as recommendations for the inter-province radius of influence in disease prevention measures and the creation of a high-quality environment.

Implementation of K-Means Clustering in Food Security by Regency in East Java Province in 2022

Food is the main need that society must fulfill. If food security is disrupted, it will have a negative impact on the nation's life. The agricultural sector has an important role in West Java Province. This province has a large area of agricultural land, so it has high potential to produce abundant agricultural production. However, knowing the adequate number of farmers is very important. Therefore, the implementation of K-Means Clustering can make a significant contribution to the East Java Provincial Agriculture Service in grouping farmers by district. To achieve optimal results, determining the best K value needs to be considered carefully. K-Means Cluster Analysis is a method of non-hierarchical Cluster Analysis that groups data into one or more groups. Data with the same characteristics is grouped into one cluster and data with different characteristics is grouped into another cluster. The data used in this research are land area and rice production in the Regency of East Java Province in 2022. Based on the results of research with the object of Food Security, it can be concluded that, the results of the analysis of the application of manual data mining calculations in Excel Software using the K-Means Clustering method, resulted in two types of clustering in the form of C0, namely the Highest Land Area and Production group with 4 districts: Jember Regency, Ngawi Regency, Bojonegoro Regency and Lamongan Regency, for C1 clustering, namely the Lowest Land Area and Production group with 25 districts in East Java Province

Cluster-Based Multinomial Logistic Regression Model for Health-Related Quality of Life Among People Living with HIV in Brazil.

Improvement in treatment options has increased the survival of people living with HIV (PLHIV). Thus, we evaluated the factors associated with better health-related quality of life (HRQoL) among PLHIV in Brazil. This was a cross-sectional study carried out among 349 PLHIV. Data were collected using an interview-based questionnaire, and HRQoL was assessed by the Brazilian version of the WHOQOL HIV BREF instrument. We used non-hierarchical cluster analysis (K-means) to compile the WHOQOL HIV BREF's overall and domain scores into a unique more multidimensional measure for HRQoL consisting of three clusters: poor, fair and good; associations with clusters of better HRQoL were assessed using multinomial logistic regression models. The mean and median overall HRQoL scores were 15.13 (SD = 3.39) and 16, respectively. The reliability and validity of the Brazilian version of the WHOQOL HIV BREF instrument was confirmed among PLHIV in a non-metropolitan, medium-sized municipality of Brazil, which reaffirmed the cross-cultural validity of this instrument. The factors male sex; heterosexual and asexual orientations; higher individual income; undetectable viral load; absence of any comorbidity and presence of an infectious or a chronic comorbidity, with mental illness as the reference; and never having consumed illegal substances were independently associated with good HRQoL. Thus, the compilation of the WHOQOL HIV BREF's overall and domain scores into a unique multidimensional measure for HRQoL, which this study proposed for the first time, may facilitate more robust interpretations and models of predictors. These differentials could simplify HRQoL as an indicator of health and wellbeing to be routinely used as a key outcome in the clinical management of patients and in the global monitoring of health system responses to HIV.

Variability Study Analysis for Half-sib Families under Control Environmental Conditions in Neolamarckia cadamba (Kadamb)

Neolamarckia cadamba is a widespread medium-sized deciduous forest tree over most of India, Bangladesh, and Sri Lanka. Twenty-half-sib progenies of kadamb (Neolamarckia cadamba) from various regions in Bihar, Rajasthan, and Uttar Pradesh were sown in a glasshouse in 2022 in order to assess the genetic diversity using non-hierarchical Euclidean cluster analysis. Significant variation was seen in the biomass and morphological traits of a few plus trees. Under greenhouse conditions, significant differences were seen in the growth parameters of plus tree progenies. Six clusters were formed from the twenty NPTs, according to the genetic divergence analysis. Clusters V and VI were found to have the greatest inter-cluster distance (5.530). The cluster (4.258) displayed the highest intra-cluster distance. As a result, there was a genetic divergence between clusters. Therefore, in order to obtain high-yielding genotypes in Neolamarckia cadamba, hybridization involving trees of clusters VI and V is advised. Collar diameter was a major factor in the creation of genetic diversity and contributed the most to the overall divergence. The superior plus trees of cluster V (NPT4, NPT5, NPT11, NPT12, NPT13, NPT17) and cluster VI (NPT18, NPT19, NPT20) may be taken into consideration as possible parents for a future tree improvement program in Neolamarckia cadamba due to their high cluster mean and large genetic distance.

Territorial sustainability performance assessment in African context: Case study of the Moroccan provinces

Territorial sustainability performance is becoming a core subject and an important governing tool for defining policies and strategies from the global to local scale. In this regard, this research has two main objectives. The first one is to develop composite indicators considering the social, environmental, and economic aspects as coupled systems for assessing the territorial sustainability performance (TSP) at the provincial level of Moroccan territory. The second objective is to identify provinces having difficulties in reaching economic growth, social equity, and environmental preservation. Using the standard constant-elasticity-of-substitution function, the TSP index was calculated based on 33 indicators, including 15, 13, and 5 for economic, social, and environmental indicators respectively. The evaluation was conducted at three levels: first; from indicators to nine dimension indices (Occupation, Education, Employment, Public Services, Health infrastructure, Tourism, Mobility, Vegetation, and Climate); second, from these dimensions to the three main aspects of sustainability; and finally, the TSP index. The results obtained from the composite index (TSPI) show that the majority of the Moroccan provinces present a low to moderate sustainability performance, requiring an effective improvement in those provinces' management. To better analyze the dimension performances and illustrate weaknesses and strengths, Moroccan provinces were subdivided into 5 main clusters using hierarchical and non-hierarchical Cluster-Analyses. The outcomes show that 62% of Moroccan provinces present some weaknesses in employment, education, and tourism. However, the public services, health infrastructure, and climate are the strengths of the remaining Moroccan provinces. These findings can be considered a valuable information system for adequate decision-making and enhancing vision in territorial planning and management.

Exploring Empty Truck Behavior Using One-Year Probe Vehicle Data

Examining empty truck behavior can be beneficial in developing an efficient and environmentally friendly logistics system. This study used probe vehicle data from April 2019 to March 2020 in Kyushu, Japan, to explore empty truck behavior. The results revealed a difference in the empty truck ratio across the day of the week, time of the day, and month and the trip distribution of empty and loaded trucks. A nonhierarchical cluster analysis was used to classify vehicles based on empty/loaded situations. These results can be potentially used to reduce the number of empty trucks and improve the efficiency of logistics systems.

[A report of five cancer screening rates and the related measures for cancer control in the workplace, surveyed by the Corporate Action to Promote Cancer Control].

Most cancer control measures in the workplace have limited supporting evidence. This study aimed to identify highly effective cancer control measures, based on a survey by the Corporate Action to Promote Cancer Control. The firms and organizations that responded to the web survey were included. The questionnaire comprised five cancer (stomach, lung, colorectal, breast, and cervical) screening rates and their countermeasures to promote cancer control. We conducted a non-hierarchical cluster analysis according to the degree of the measures and compared the screening rates among each group using an analysis of variance. Then, we performed two multiple regression analyses with the mean screening rates for stomach/lung/colorectal cancer and breast/cervical cancer as dependent variables, the implementation of each countermeasure as an independent variable, and the size and industry as control variables. We obtained responses from 704 firms and organizations. The three groups classified by cluster analysis were defined as active, moderate, and negative. For all cancer screenings, the main effects were significant, and multiple comparisons revealed that the difference between the active and negative groups (ts > 3.30, ps < 0.01, Hedges' ds > 0.73) and the moderate and negative groups (ts > 3.70, ps < 0.01, Hedges' ds > 0.88) were significant. For the four cancers other than lung, the difference between the active and moderate groups was not significant (ts < 0.21, ps < 0.84, Hedges' ds < 0.02), and for lung, the difference was significant, but the effect size was small. The multiple regression analyses revealed that "distribution of colorectal cancer test kits to all subjects" (β = 0.14) was significant for stomach, lung, and colorectal cancer, while "financial supports for cancer screening" (β = 0.24), "screening as part of employment" (β = 0.18), and "careful screening of female subjects" (β = 0.17) were significant for breast and cervical cancer, respectively. We identified effective countermeasures for cancer control in the workplace, and these measures will help increase cancer screening rates.

A Statistical review of the dates and patterns of volcanic activity of Lewotolo Volcano, East Nusa Tenggara, Indonesia

Lewotolo is a stratovolcano located on the Lembata Island, East Nusa Tenggara (Lesser Sunda Islands), Indonesia. The first geohistory of the Lewotolo volcanic eruption was recorded in 1660 (Volcanic Eruption Index (VEI) 3). Since November 11, 2020, Lewotolo has been included in the list of Indonesian volcanoes with the necessary precautions, comparable to the Anak Krakatau, Merapi, and Semeru volcanoes. We investigated Lewotolo volcanic activity by analyzing the height of the ash column data, maximum seismograph amplitude, and recorded seismic duration from November 29, 2020, to September 23, 2022, which are provided in magma.esdm.go.id. The results showed 191 records of eruption activity data, which formed three clusters for each variable using the Elbow Method in the Non-Hierarchical K-means clustering analysis. These data were plotted on the solar and Hijri lunar calendars. The data plot shows anomalies in the volcanic activity frequency and a significant pattern of activity at specific times. The data plot illustrates that the value of the activity frequency pattern tends to increase at Earth orbital events such as the December-January perihelion (up to 0.124) and June-July aphelion (up to 0.262) and at the new moon phases (up to 0.168). This phenomenon appears as solar and lunar tidal anomalies that commonly occur as gravitational sea tides. Along with the need for more comprehensive data, the results of this study may provide new perspectives for further research on the possible role of gravitational tide phenomena in volcanic activity, at least to explain the volcanic activity in Lewotolo.

Physiological characterization of Carica papaya L. variety UN Cotové

The application of plant physiological principles in genetic improvement allows to approach with greater ease, the solution of the limitations of the agricultural production in an integral and articulated way. For this reason, physiological indicators were used as criteria for the selection of UN Cotové papaya variety plants, which serve as parents for a species breeding program. Eighteen individuals were selected for monthly gas exchange measurements, chlorophyll fluorescence, and leaf area index. The data analysis was done with multivariate non-hierarchical clustering and factorial analysis. Two clusters were generated that did not group morphotypes regarding the gas exchange, quantum yield of Photosystem II (PSII), and leaf area index (LAI). ANOVA established significant differences between plants for most variables. The means were compared with the Tukey test (P &lt; 0.05). Six plants presented better yield, positioning them as a promising material to obtain a papaya variety with outstanding characteristics under tropical dry forest (TDF) conditions.

Coagulation biomarkers and coronavirus disease 2019 phenotyping: a prospective cohort study

BackgroundBecause severe acute respiratory syndrome coronarivus 2 (SARS-CoV-2) leads to severe conditions and thrombus formation, evaluation of the coagulation markers is important in determining the prognosis and phenotyping of patients with COVID-19.MethodsIn a prospective study that included 213 COVID-19 patients admitted to the intensive care unit (ICU) the levels of antithrombin, C-reactive protein (CRP); factors XI, XII, XIII; prothrombin and D-dimer were measured. Spearman’s correlation coefficient was used to assess the pairwise correlations between the biomarkers. Hierarchical and non-hierarchical cluster analysis was performed using the levels of biomarkers to identify patients´ phenotypes. Multivariate binary regression was used to determine the association of the patient´s outcome with clinical variables and biomarker levels.ResultsThe levels of factors XI and XIII were significantly higher in patients with less severe COVID-19, while factor XIII and antithrombin levels were significantly associated with mortality. These coagulation biomarkers were associated with the in-hospital survival of COVID-19 patients over and above the core clinical factors on admission. Hierarchical cluster analysis showed a cluster between factor XIII and antithrombin, and this hierarchical cluster was extended to CRP in the next step. Furthermore, a non-hierarchical K-means cluster analysis was performed, and two phenotypes were identified based on the CRP and antithrombin levels independently of clinical variables and were associated with mortality.ConclusionCoagulation biomarkers were associated with in-hospital survival of COVID-19 patients. Lower levels of factors XI, XII and XIII and prothrombin were associated with disease severity, while higher levels of both CRP and antithrombin clustered with worse prognosis. These results suggest the role of coagulation abnormalities in the development of COVID-19 and open the perspective of identifying subgroups of patients who would benefit more from interventions focused on regulating coagulation.

Broad-Scale Environmental Factors Determining Fish Species Composition of River Estuaries in the Japanese Archipelago

This study was conducted to clarify the relationship between fish species composition and environmental factors at various scales in river estuaries in the Japanese archipelago. The estuaries harbor diverse fish species composition due to their complex topography, varied climatic zones, and ocean currents. Non-hierarchical cluster analysis classified the fish species composition of 100 river estuaries (58 families, 152 species, and 486,356 individuals) into five groups. The highest number of species was confirmed in the group of river estuaries located in southwestern Japan and flowing into the inner bay (55.1 ± 9.6 species), characterized by an abundance of the number of Gobiidae species. In contrast, the species number was the lowest in the groups consisting of river estuaries that primarily belong to the northern Japanese archipelago (15.3 ± 3.3 species) that were characterized by an abundance of the number of cold-water fishes such as Salmonidae, Pleuronectidae, and Osmeridae. The highest fish species diversity was confirmed in the groups consisting of river estuaries flowing into the inner bays of southwestern Japan, which had a large tidal range (344 ± 132 cm), suggesting that the large tidal differences create a diverse tidal flat environment that supports fish species diversity. These results indicate that regional species diversity and geographical factors influence the classification of fish fauna. In northeastern Japan, the classification was influenced by geographic area and associated water temperature changes, whereas in southwestern Japan, tidal differences and wave effects could be more important. Understanding the factors that influence the fish species composition of a target river is fundamental to estuarine conservation, and the findings of this study provide basic knowledge for conservation.

Russo-Ukrainian War and Trust or Mistrust in Information: A Snapshot of Individuals’ Perceptions in Greece

The purpose of this study was to assess the Greek public’s perceptions of the reliability of information received about the Russo-Ukrainian war in the spring of 2022. The study was conducted through an online questionnaire survey consisting of closed-ended statements on a five-point Likert scale. Principal components analysis was performed on the collected data. The retained principal components (PCs) were subjected to non-hierarchical k-means cluster analysis to group respondents into clusters based on the similarity of perceived outcomes. A total of 840 responses were obtained. Twenty-eight original variables from the questionnaire were summarised into five PCs, explaining 63.0% of the total variance. The majority of respondents felt that the information they had received about the Russo-Ukrainian war was unreliable. Older, educated, professional people with exposure to fake news were sceptical about the reliability of information related to the war. Young adults who were active on social networks and had no detailed knowledge of the events considered information about the war to be reliable. The study found that the greater an individual’s ability to spot fake news, the lower their trust in social media and their information habits on social networks.

Multivariate analysis of post-transition OECD countries in the context of inequality measures

The aim of this paper is to classify the post-transition OECD countries according to the Gini coefficient for income inequality, the S80/S20 ratio, the income share of the bottom 40% of the population, educational attainment – tertiary education, and labor force participation rate using factor and cluster analyses. Factor analysis resulted in two extracted factors, and factor scores were calculated. Hierarchical and non-hierarchical cluster analysis was performed on factor scores to classify eight posttransition OECD countries and three candidate countries. The research question of this paper is to investigate whether there are similarities/differences between existing members and candidates for membership in the OECD, among the selected post-transition countries of Europe, in the context of income inequality. Based on the dendrogram obtained by the hierarchical Ward’s method, a three cluster solution was selected. The non-hierarchical k-means method for the three-cluster solution clustered Croatia with Bulgaria and Romania. These three countries are OECD candidate countries. Our findings confirm that the three candidate countries remain behind because of historical reasons and the non-implementation of structural reforms.

SEGMENTATION OF FOOTBALL FANS BASED ON EVANGELISTIC BEHAVIOUR: EMPIRICAL EVIDENCE FROM CROATIA

Professional football is a big business in which understanding fans is a precondition for the long-term commercial success of clubs. The focus of this paper is on the segmentation of football fans based on their behaviour and on determining the differences among the identified segments. The research is cross- sectional, and the data was collected through an online survey of 370 fans, sampled using a non-probability, convenience sample. For segmentation purposes, the eFangelism scale – consisting of four basic forms of evangelistic behaviour: advertising, advocating, assimilating, and antagonizing, was used. Data were analysed using hierarchical and non-hierarchical cluster analysis, ANOVA, and posthoc Tukey test. The results of this study confirm the applicability of the concept of sports fan evangelism (eFangelism). Four clusters of football fans are identified based on evangelistic behaviours. This study provides concise profiles for each of the clusters and shows differences between the characteristics of their members. The findings from this study can be of use to sports marketers – namely, elements of marketing strategy can and should be adjusted for idiosyncrasies of various segments identified through the study.

KLASTERISASI DESTINASI WISATA BERDASARKAN KOORDINAT GPS DENGAN METODE KERNEL K-MEANS

Although K-Means Clustering, a sort of non-hierarchical cluster analysis, is frequently employed, it has limitations when processing data with non-linearly separable characteristics (no distinct boundaries) and overlapping clusters, or when visually comparing the results of clusters to those of other clusters. When dealing with data that have overlapping and non-linearly separable cluster features, the Gaussian Kernel function in Kernel K-Means Clustering can be applied. The input data must be displayed using kernel functions in a new dimension, which is where Kernel K-Means Clustering and K-Means diverge.&#x0D; The purpose of this study is to determine the concept of steps and the results of Kernel K-Means Clustering analysis to classify tourist destinations in the Pesonajawa database based on GPS coordinates. The data used was obtained from the PesonaJava.com database, which consists of 28 tourist destinations located on the island of Java. The silhouette score is used in this study to evaluate the cluster results.&#x0D; The evaluation of the cluster results based on the research results revealed that the best number of clusters was K = 6, with a sigma value of = 5.