Non-hierarchical Clustering Methods Research Articles

Climate Classification for Major Cities in China Using Cluster Analysis

Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China’s traditional geographical division along the Qinling–Huaihe River line.

Investigation of Earthquakes in Turkey with Cluster Analysis

In this study, earthquakes with a magnitude of 3 and above occurring between 36-42 degrees North latitude and 26-45 degrees East longitude, within and around Turkey from 1980 to 2022, were considered. Turkey and its immediate surroundings were divided into 114-unit squares using latitude and longitude coordinates to investigate seismic similarities. The variables including 50130 earthquake depths and magnitudes in our data set were analyzed using the {K-Means Cluster Analysis} method in the SPSS program. Hierarchical clustering method was used to determine the number of clusters and the number of clusters was determined as 5 with the help of the dendrogram obtained. As a result of the cluster analysis, it was observed that 47% of the earthquakes in Turkey were at a depth of 5 to 10 km. It has been observed that 93% of the earthquake intensities occur between 3 and 4 magnitudes. A cluster analysis was conducted to assess the similarities and dissimilarities among earthquakes within these unit squares in terms of their magnitudes and depths. Due to the large sample size and the analysis involving continuous variables, the earthquake data were grouped into 5 clusters using non-hierarchical clustering methods, and the relationships within and between these clusters were observed.

Perceived risk-based sport spectator segmentation during the pandemic situation

PurposeAlthough many attempts to discover key segments of sport spectators have been extant, little segmentation effort has been made to reflect pandemic situations such as the COVID-19 pandemic. The purpose of this research is twofold: (1) to classify sport spectators into key segments based on perceived risks associated with a mass-gathered sporting event during the COVID-19 pandemic and (2) to identify each segment’s profiles.Design/methodology/approachQuestionnaire surveys of spectators attending a Japanese rugby game during the COVID-19 pandemic (January–June 2021) were conducted (n = 1,410). A combination of hierarchical and non-hierarchical clustering methods was executed.FindingsThe results revealed the five-cluster solution as the optimal number of clusters representing the samples (i.e. spectators with extremely low-risk perception, those with low-risk perception, those with moderate-risk perception, those with high-risk perception and those with higher social risk perception). This five-cluster solution showed sufficient stability and validity. Moreover, each segment had different profiles regarding three background aspects – demographics, psychographics and behavioral variables.Originality/valueThis study is the first effort to segment sport spectators based on perceived risks associated with a mass-gathered sporting event in the pandemic situation. Despite extensive segmentation studies to explore sport fans, contribution reflecting the post-crisis situations is scant. Therefore, the findings provide insight into this realm by providing a new viewpoint for understanding sport spectators during a possible future pandemic era.

A TWO-STEP CLUSTER FOR CLASSIFYING PROVINCES IN INDONESIA BASED ON ENVIRONMENTAL QUALITY

The main objective of this study was to conduct a cluster analysis of the environmental health index in Indonesia for all the provinces. Clustering the environmental health index was important to reveal regional disparities, target and intervention policies, monitor progress over time, and allocate resources more effectively for improved environmental health outcomes. In this study, a sample of 34 units was utilized, encompassing all provinces in Indonesia. The environmental health index was clustered based on five indicators, namely Water Quality Index, Air Quality Index, Soil Quality Index, Marine Quality Index, and Land Cover Quality Index. This research used the two-stage clustering method, which was unique in combining both hierarchical and non-hierarchical clustering methods to produce a more accurate and reliable solution. Four clusters were determined to group provinces in Indonesia based on the environmental health index. The analysis found that the quality of clustering was in the fair but close to good category. The clustering results showed that 32% of the provinces were in cluster 4 and 26.5% of the provinces were in cluster 1. Then, 23.5% and 17.6% of the provinces were in clusters 2 and 3, respectively. In addition, two indicators were found to be the most predictive of the overall clustering solution, namely the Soil Quality Index and the Land Cover Quality Index. The results also implied that provinces in cluster 3 had the lowest environmental quality so they must improve it by looking at provinces in cluster 4, which was the group with the best environmental quality index.

K-means Nonhierarchical Cluster and Dbscan Outlier Detection In the Grouping of Stock Issuers

Group analysis aims to group objects based on similar characteristics so that they are in one group homogeneous and between groups heterogeneous. Study this aim group issuer share in Indonesia based on volatility, liquidity, and market capital. This study uses the non-hierarchical K-Means Clustering method, because the number of samples is big and the number of groups are known. The K-Means Clustering grouping method produces as many as 6 groups with different characteristics. 2. Group 1 consists of stock issuers with quite high volatility and liquidity. The characteristic of group 2 is that it consists of stock issuers with the lowest volatility. Big Capital is the nickname for group 3 because it has market capital or the asset value is very large among all groups and the volatility is very small, and liquid. In Group 4, stock issuers have the highest volatility and the lowest liquidity. Results of profile interpretation in group 5, issuer’s stocks have the highest liquidity and market capital is quite low. Share issuers in group 6 have the volatility highest. Group 3 is recommended as an option for investing. Because, having market capital or large asset values, liquid, and volatility is low enough to minimize risk. The originality of this research is that there is no combination of methods between grouping in fields with k-means clustering and detection of sales with DBSCAN, especially in the field of issuer share in Indonesia.

Human Development Clustering in Indonesia: Using K-Means Method and Based on Human Development Index Categories

The quality of life for Indonesia's population can be measured from the human development index in each province. People who have a good quality of life indicate a prosperous life. The government has the responsibility to advance the welfare of the nation under the mandate of the constitution. The clustering of the human development index (HDI) in Indonesia is used to determine the distribution of quality of life or the distribution of social welfare. In this study, the K-Means method, which is a popular non-hierarchical clustering method, is used to classify human development in each province based on HDI indicators, namely Expected Years of Schooling, Mean Years of Schooling, Adjusted Per Capita Expenditure, and Life Expectancy at Birth. Provinces in Indonesia are clustered into 4 clusters. These results were also compared with the clustering based on HDI categories determined by Statistics Indonesia based on certain cut-off values. According to the HDI category, provinces in Indonesia fall into the medium, high, and very high categories. The results of the two groupings show that there is a trend toward appropriate characteristics for each group. Thus, K-Means can classify provinces in Indonesia according to the characteristics of the HDI indicators.

Social infrastructures and socio-economic vulnerability: A socio-territorial integration study in Spanish urban contexts

The growing processes of the socio-economic inequality resulting from the neoliberal political model are evident in large Spanish cities, with a spatial dimension that allows us to visualise its contradictions with great clarity. These social contradictions are particularly marked among the most vulnerable and foreign population groups. For this reason, the study of the spaces and services termed Social Infrastructures (SI), as facilities which favour social integration and cohesion in the city, is of special importance. Our study addresses the distribution of SI in the Spanish cities of Madrid and Barcelona, their relationship with the level of vulnerability in the neighbourhoods and the distribution of the Spanish and foreign population in a novel way, by creating a synthetic SI index and using a non-hierarchical clustering method. Our results show two completely different city models: in Barcelona, SI are distributed following the criterion of socio-territorial equity, while in Madrid, there is a greater presence of SI in the wealthier areas. This leads us to reflect on the social and political implications of our results in both cities.

METHOD OF BUILDING ENSEMBLES OF MODELS FOR DATA CLASSIFICATION BASED ON DECISION CORRELATIONS

The scientific work highlights the problem of increasing the accuracy of binary classification predictions using machine learning algorithms. Over the past few decades, systems that consist of many machine learning algorithms, also called ensemble models, have received increasing attention in the computational intelligence and machine learning community. This attention is well deserved, as ensemble systems have proven to be very effective and extremely versatile in a wide range of problem domains and real-world applications. One algorithm may not make a perfect prediction for a particular data set. Machine learning algorithms have their limitations, so creating a model with high accuracy is a difficult task. If you create and combine several models by combining and aggregating the results of each model, there is a chance to improve the overall accuracy, this problem is dealt with by ensembling. The basis of the information system of binary classification is the ensemble model. This model, in turn, contains a set of unique combinations of basic classifiers – a kind of algorithmic primitives. An ensemble model can be considered as some kind of meta-algorithm, which consists of unique sets of machine learning (ML) classification algorithms. The task of the ensemble model is to find such a combination of basic classification algorithms that would give the highest performance. The performance is evaluated according to the main ML metrics in classification tasks. Another aspect of scientific work is the creation of an aggregation mechanism for combining the results of basic classification algorithms. That is, each unique combination within the ensemble consists of a set of basic models (harbingers), the results of which must be aggregated. In this work, a non-hierarchical clustering method is used to aggregate (average) the predictions of the base models. A feature of this study is to find the correlation coefficients of the base models in each combination. With the help of the magnitude of correlations, the relationship between the prediction of the classifier (base model) and the true value is established, as a result of which space is opened for further research on improving the ensemble model (meta-algorithm)

Analysis of The Theme Clustering Algorithm Using K-Means Method

The title of this research is the analysis of the thesis theme clustering algorithm using the k-means method. The main problem is how we can find out which theme is most in demand by thesis students at the Faculty of Engineering, University of Muhammadiyah Bengkulu. This clustering uses the K-means method. The K-Means method was chosen because this method is one of the non-hierarchical data clustering methods that seeks to partition data into two or more clusters with the same characteristics included in the same cluster. The purpose of this research is to help prospective students who will write their thesis in knowing which themes are more interested in them.

Implementasi Metode K-Means Clustering Pada Segmentasi Citra Digital

ABSTRACT:Image segmentation is the process of placing a label for each pixel in an image therefore pixels with the same label share certain visual characteristics. One of the algorithms that can be applied in accelerating the segmentation process is K-Means Clustering. K-means is a non-hierarchical clustering method that tries to partition existing data into one or more clusters. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. The implementation of the system uses the Visual Basic 2010 programming language and the method used in this research is the waterfall method. The results of the analysis carried out show that the similarity of the identified images based on the proximity of the color values ​​and the accuracy produced is quite good, especially for objects that have special colors or colors that have become characteristics of the object.
 Keywords: Digital Image, Segmentation, Clustering, K-Means

Wavelet-based fuzzy clustering of interval time series

We investigate the fuzzy clustering of interval time series using wavelet variances and covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional hierarchical and non-hierarchical clustering methods lead to the identification of mutually exclusive clusters whereas fuzzy clustering methods enable the identification of overlapping clusters, implying that one or more series could belong to more than one cluster simultaneously. An interval time series (ITS) which arises when interval-valued observations are recorded over time is able to capture the variability of values within each interval at each time point. This is in contrast to single-point information available in a classical time series. Our main contribution is that by combining wavelet analysis, interval data analysis and fuzzy clustering, we are able to capture information which would otherwise have not been contemplated by the use of traditional crisp clustering methods on classical time series for which just a single value is recorded at each time point. Through simulation studies, we show that under some circumstances fuzzy c-medoids clustering performs better when applied to ITS than when it is applied to the corresponding traditional time series. Applications to exchange rates ITS and sea-level ITS show that the fuzzy clustering method reveals different and more meaningful results than when applied to associated single-point time series.

Pengelompokan Hasil Evaluasi Pembelajaran Metode Hafalan Al Qur’an Tawazun Menggunakan Metode K-Means

The method of memorizing the Qur'an Tawazun is a method that maximizes the use of the right brain and left brain, allowing a person to memorize, understand, and believe. Each category has several assessment points as a benchmark for the ability of students, which are used to overcome the level of failure of students in each category of learning the method of memorizing the Qur'an tawazun. The results of the evaluation of the learning of the tahfidz Islamic boarding school Daarul Huffadz Indonesia in 2020 were felt to be less than optimal, because the learning process was carried out simultaneously. This can be seen from the difference in scores that are quite different in each category of assessment. Based on the previous problem, it is necessary to group the results of the evaluation of learning the Qur'an memorization method. The goal is that every student gets maximum treatment and provides convenience for the institution, as well as teaching staff to carry out learning. The purpose of this study is to determine the optimum number of clusters as well as members of each cluster by measuring cluster performance using the Davies Bouldin Index (DBI) method and implementing the K-Means algorithm. The K-Means algorithm is a non-hierarchical data clustering method that is able to group large amounts of data, relatively quickly, and efficiently. This study uses 401 observational data and 12 attributes. From the calculation results, the optimal number of clusters lies in 2 clusters, with a Davies-Bouldin Index (DBI) value of 1.439. There are 26 members of cluster 1, and 375 members of cluster 2.

Inventory Grouping to Support IT Business Management with the K-Means Algorithm

Inventory control is an important factor in a company that functions to maintain smooth production. Inventory control is the most important activity in the survival of the company. Fulfillment of stocks of goods or products and recording transactions manually. The fluctuating number of requests from consumers resulted in the stock that Gaptech computer had to prepare to become unstable. In addition, the various and many types of products made stock management inaccurate. by extracting data using one of the data mining methods, namely data grouping. In this study grouping sales reports on Gaptech computer, the data is grouped into 2 groups using one of the data grouping algorithms, namely the K-Means Algorithm. The K-Means algorithm is a non-hierarchical data clustering method that divides data into one or more clusters, so that data with the same characteristics are grouped in the same cluster. The grouping results are used to find out which product groups are in great demand and which are less desirable. The results of this study will produce a software that is created using a method of managing incoming transaction data so that it will make it easier for Gaptech computer store owners to manage stock items.

Penerapan Clustering K-Means untuk Pengelompokan Tingkat Kepuasan Pengguna Lulusan Perguruan Tinggi

One way to evaluate the quality of graduates is to provide questionnaires to graduate users, namely agencies / companies in the world of work in order to assess the quality of graduates of each university. Questionnaires for graduates are generally carried out by filling out the questionnaire form physically and then returning to the college. The K-Means method is one of several non-hierarchical clustering methods. Data clustering techniques are easy, simple and fast. Many approaches to creating clusters or groups, such as creating rules that dictate membership in the same group/group based on the level of similarity between the members of the group. Other approaches such as creating a set of functions to measure multiple criteria from grouping as a function of some parameters of clustering/grouping. From the results and discussions, K-Means clustering succeeded in grouping graduate user satisfaction data into three clusters where the results shown by manual calculations and applications showed the same results where clusterS C1 as many as 48 alternatives, C2 as many as 1 alternative, and C3 as many as 2 alternatives. In the sense that the application that is built successfully implements K-Means clustering is evidenced by the comparison of applications with weka tools has similar percentage results. In terms of the percentage of graduate users or alumni from STMIK PPKIA Tarakanita Rahmawati 94.12% Very satisfied, 1.96% Satisfied and 3.92% Quite Satisfied.

Clustering Pelaksanaan Vaksinasi di Jawa Tengah Menggunakan Metode K-Means

Vaccines are a form of government responsibility in guaranteeing citizens' rights to health. In its implementation, the government seeks to fulfill the availability of vaccines for at least 208,265,720 residents. This number is the minimum number to be able to achieve herd immunity. To make it easier to show the vaccine achievements of each region, clustering can be done. The K-Means method is a non-hierarchical clustering method that is performed by partitioning data into predefined clusters. The research objects are 35 cities/districts in Central Java. The results of the data processing show that there are 2 optimal clusters, with information that cluster 1 is an area with a high vaccination achievement, while cluster 2 is an area with a low vaccination achievement.

Healthy Food Consumer Segmentation for Targeting and Positioning New Product Slimming Jelly

This study aims to determine market segments, market targets, and market positioning of slimming jelly using the segmentation analysis on healthy food consumer. The respondents used in this study were the consumers who regularly consume healthy food at least once per week within the last two months. The number of samples is 204 respondents. The consumer segmentation is done based on several variables including demographic aspects, motivation, consumption consistency, social tendencies, and attitudes towards new products. The segmentation analysis is carried out using the K-means non-hierarchical clustering method. The method splits the customers into three significantly different clusters at the 0.05 level, namely the adolescent age segment, which has attention to body shape, the adult segment which focus on body weight control, and the adults' segment which are not interested on body weight control. Based on the principle of selective specialization, the target market for slimming jelly is in clusters 1 and 2, with slimming jelly is positioned as a hunger delaying food that claimed to be high in fiber and low in calories so that it is suitable for health and can be used in diet programs.

Klasterisasi Desa dengan Menggunakan Algoritma K-Means pada Data Potensi Desa

The Developing Village Index (Indeks Desa Membangun) is a composite index compiled based on three indices, namely the Social Resilience Index, the Economic Resilience Index and the Village Ecological Index. Based on the Developing Village Index, there are 5 classifications of villages, namely Independent Villages, Developed Villages, Developing Villages, Underdeveloped Villages, and Very Underdeveloped Villages. In line with that, the Central Statistics Agency also issued a Village Development Index (Indeks Pembangunan Desa) to show the classification of the village. This study aims to determine village clustering using the K-Means Algorithm on the 2020 Village Potential (Podes) data, especially on the economic dimension. K-Means is a non-hierarchical data clustering method that can partition data into two or more groups. The number of clusters is determined based on the number of The Developing Village Index statuses, which are 5 clusters. The results of 6 iterations of calculations using the K-Means algorithm show that cluster 1 is grouped into the status of Very Underdeveloped Villages as many as 8 Villages. Then in cluster 2 it is grouped as Underdeveloped Villages as many as 3 Villages. Furthermore, in cluster 3, it is grouped as Developing Villages as many as 83,987 Villages. In cluster 4, they are grouped as Developed Villages as many as 24 Villages. Then in cluster 5 it is grouped as Independent Villages as many as 16 Villages

Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.

Algoritma K-Means Clustering dalam Mengklasifikasi Data Daerah Rawan Tindak Kriminalitas (Polres Kepulauan Mentawai)

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Mentawai Islands Police, which is an agency that can provide security and protection for the community, especially those in the Mentawai Islands Regency. The problem is that it is difficult for the Mentawai Islands Police to classify areas that are prone to crime in the most vulnerable, moderately vulnerable and not vulnerable categories. Especially considering the condition of the Mentawai, there are four large islands consisting of 10 sub-districts, where crime is increasing every year, especially those in the Mentawai Islands Regency area such as motor vehicle theft. Based on the background of the problem above, the researcher is interested in taking research in creating a system to predict the crime rate in the Mentawai Islands Regency in order to anticipate the surge in crime that will come. The method used is the K-Means Clustering Algorithm as a non-hierarchical data clustering method to partition existing data into one or more clusters or groups. This method partitions data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into other clusters. Clustering is one of the data mining techniques used to get groups of objects that have common characteristics in large enough data. The data used is data on cases of criminal theft of motor vehicles for the last 5 years from 2016 to 2020. The results of the test show that South Sipora District is an area prone to the crime of motor vehicle theft.

Systematic Literature Review: Penggunaan Algoritma K-Means Untuk Clustering di Indonesia dalam Bidang Pendidikan

K-Means is a non-hierarchical data clustering method that can group data into several clusters based on data similarity, so that data with the same characteristics are grouped in one cluster and data with different characteristics are grouped in another cluster. The K-Means method can be used to process various data, including for clustering in the field of education. The use of the K-Means algorithm has been widely carried out but not many activities have been handled and are only often used for selection or acceptance activities and the use of attributes that must be reproduced to get optimal results. In this study, we will review various papers that perform clustering using the K-Means method for research in the field of education. Based on our research, papers related to the use of the K-Means algorithm for clustering in education have proved feasible and useful for future research. So it can be concluded that the K-Means method has been tested to be used for clustering in the field of education and the K-Means method can be useful in many aspects of education, both having an impact on educators, students and other educational aspects.