Hierarchical Clustering Method Research Articles

Unsupervised Machine Learning Revealed that Repeat Transcranial Magnetic Stimulation is More Suitable for Stroke Patients with Statin.

Repeat transcranial magnetic stimulation (rTMS) demonstrates beneficial effects for stroke patients, though its efficacy varies due to the complexity of patient conditions and disease progression. Unsupervised machine learning could be the optimal solution for identifying target patients for transcranial magnetic stimulation treatment. We collected data from ischaemic stroke patients treated with rTMS. Unsupervised machine learning methods, including K-means and Hierarchical Clustering, were used to explore the clinical characteristics of patients suitable for rTMS. We then utilized a prospective observational cohort to validate the effect of selected characteristics. For the validated cohort, outcomes included the presence of motor evoked potentials (MEP), favorable functional outcomes (FFO), and changes in the Fugl-Meyer Assessment (FMA) at 3 and 6months. Hierarchical clustering methods revealed that patients in the better prognosis group were more likely to take statins. The validated cohort was grouped based on statin intake. Patients taking statins exhibited a higher rate of MEP (p = 0.006), a higher rate of FFO at 3months (p = 0.003) and 6months (p = 0.021), and a more significant change in FMA (p < 0.001) at both 3 and 6months. Statin intake was associated with FFO and changes in FMA at 3 and 6months. This relationship persisted across all subgroups for FMA changes and some FFO subgroups. Stroke patients undergoing rTMS treatment taking statins exhibited greater MEP, FFO, and changes in FMA. Statin intake was associated with a better prognosis in these patients.

Method for Determining Radioresistance of Cancer Cell Lines Based on Cluster Analysis of Clonogenic Cell Survival Data

Background: The outcome of radiation therapy, the duration and quality of life of cancer patients significantly depend on the radiosensitivity (RS) of a cancerous tumor, and the duration of the patient’s relapse-free period is largely determined by the degree of its radioresistance (RR). Today the results of molecular mechanism investigation of cancer radioresistance and the classification of cancer cells according to their radiophenotypes mostly contribute to improving prognosis methods of treatment outcomes and increasing effectiveness of radiation therapy. In this work, we developed a classification method of cancer cells according to their radiosensitivity using machine learning based on the data analysis of clonogenic cell survival under ionizing radiation. Material and methods: The method consists of clustering parameters of experimental dose-effect relationships, which were approximated using the equation of a linear-quadratic (LQ) model, which is used to evaluate RS of cancer cells in radiobiology. The training of the statistical model included published experimental dataset of 96 cancer cell lines, for which parameters a, b and their ratio a/b of the LQ model were determined. Classification of cancer cells according to their radiosensitivity was carried out based on principal component analysis (PCA) in the parameter space (a, a/b), k-means clustering and hierarchical clustering methods. Results: Application of the developed statistical model to a large dataset of cancer cells made it possible to reliably separate radiosensitive and radioresistant (RR) cells into two clusters according to the parameters a and a/b. Application of the model to cancer cells with acquired RR, in which RS was suppressed as a result of exposure to irradiation or hypoxia, allowed tracing the shift of parent cells’ parameters from the RS cluster to the RR cell cluster. To study the genetic mechanisms of radiosensitivity, we performed bioinformatic analysis of the mutation distribution in genes encoding proteins in the cellular signalling pathways of cancer cells, i.e. proliferation, apoptosis, repair of damaged DNA molecules and antioxidant defence cellular system. Conclusion: The developed statistical model of radiophenotypic classification of cancer cells based on their radiosensitivity can be used in the development of radiation therapy treatment plans taking into account radiosensitivity of patient’s tumour. The model may be also helpful in a joint analysis of the phenotypic and genotypic characteristics of cancer cells, aiming at the elucidation of the molecular and genetic mechanisms of radiosensitivity and development of biomarkers of radioresistance.

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

To address the adverse effects of nonlinearity and dynamic time-varying in complex chemical processes on the accuracy of the soft sensor model, a local-semi-supervised ensemble learning for soft sensor modeling (local semi-supervised-selective ensemble learning-long short term memory, LS-SEL-LSTM) method is proposed in this article. Firstly, a hierarchical clustering method incorporating spatiotemporal criteria is proposed to reduce the influence of nonlinearity in global model prediction accuracy. The method considers the dynamic time-varying characteristics of temporal data and generates multiple local datasets. Then, to address the issue of multi-rate between auxiliary variables and dominant variables, a semi-supervised weight fusion mechanism based on temporal correlation is proposed, which effectively utilizes auxiliary variables to reconstruct local semi-supervised datasets and establishes local soft sensing models using LSTM. Concurrently, the parameters of the established model were optimized using the flower pollination algorithm. Subsequently, a selective ensemble learning method based on sub-model prediction accuracy and an adaptive combination weight calculation method for sub-models were proposed to improve the prediction accuracy. Finally, the effectiveness of the proposed method was verified through the actual dataset of the sulfur recovery process. The results indicate that LS-SEL-LSTM performs well in handling complex chemical processes with nonlinear and dynamic time-varying characteristics.

Experimental and Biological Approaches for Genotype X Environment Interactions Estimation for Wheat Genotypes Evaluated Under Multi Locational Trials

Genotypes VL907, HS562, HPW484 were ranked as topped three in comparison to the other during the evaluation of nine wheat genotypes at major locations of the north hills zone of the country under rain fed conditions. The least values of AMMI stability measure (ASV) had expressed the desirability of HPW484, HS562, VL2041 genotypes whereas the genotypes HS562, HPW484, VL2041 had been identified by least values of Modified Ammi Stability Value (MASV). The minimum value of simultaneous selection index measure based on the MASV (ssiMASV) had selected HS562, HPW484, VL2041 wheat genotypes while values of ssiWAASB measure found the suitability of HPW484, HS562, HS691 wheat genotypes. The composite non parametric measure NPi (2) had favoured the VL892, HS562 genotypes and values of NPi (3) measure had settled for VL892, HS562 genotypes while VL892, HPW349 wheat genotypes had been pointed by the last composite measure NPi (4). The Ward’s method of Hierarchical Clustering had placed the VL907 genotype in a separate group as compared to others. The shorter rays of measures IPC2, IPC5, IPC3, SD had reflected the less contribution of the joint effects of genotypes and measures in the biplot analysis. Non parametric composite measure NPi (1) had expressed tight direct relation with Si1,Si3,Si4,Si5,Si6,Si7 values. The values of IPC6 &amp; IPC4 had maintained the direct association with BLUP based analytic measures HMGV, RPGV, HMPRVG*Meanb, GAI, Meanb, RPGV*Meanb values. Moreover the values of CV measure had clustered with Si2, Si3, Si4,Si5, Si7 measures of this study.

Landscape of Digital Technologies Used in the National Health Service in England: Content Analysis.

In England, digital technologies are exploited to transform the way health and social care is provided and encompass a wide range of hardware devices and software that are used in all aspects of health care. However, little is known about the extent to which health care providers differ in digital health technology capabilities and how this relates to geographical and regional differences in health care capacities and resources. This paper aims to identify the set of digital technologies that have been deployed by the National Health Services clinical commissioning groups (NHS CCGs) in England. In doing this, we respond to calls to shed light on the internal dynamics and variation in the form of digital capability in England in terms of health service regional differences and health diversity, equity, and inclusion. We collected 135 annual reports that belong to 106 NHS CCGs in England, comprising more than 18,000 pages in total, released from 2020 to 2021. Using this data set, we identified 2163 pages related to digital technologies and labeled them using content analysis. We follow the construct taxonomy used by digital options theory, a theory from the management information systems field analyzing organizational resource investment choices, in classifying observed technologies according to digital themes-inherent design patterns that we identified and explained. We then used a hierarchical clustering method to extract groups of NHS CCGs that implement similar technology themes. We found 31 technologies from the reports and grouped them into 9 digital themes. The 9 themes were further assigned to 1 of the 3 constructs of digital options theory, the identification of patients' requirements (we identified information portals [76/106], digital health engagement [67/106], and digital inclusion support [45/106]), the development of new work patterns (we identified telehealth [87/106], telemedicine [35/106], and care home technologies [40/106]), the realization of improvements in efficiency and public accessibility (we identified online booking [26/106], online triage [104/106], and digital mental health services [74/106]). The 3 clusters of CCGs are identified based on the 8 themes (Hopkins=0.9914, silhouette=0.186), namely (1) digitally disengaged, (2) digitally engaged, and (3) digital torchbearer. Our findings show prominent digital themes within each construct group, namely information portals, telehealth, and online triage, covering people's fundamental health information needs. Almost half of CCGs fell into the digitally disengaged group, and all London CCGs (5/106) belonged to this group. We propose that practitioners should offer specialized assistance to regions with limited digital engagement, emphasizing digital health literacy, inclusion support, and ongoing evaluation, rather than concentrating solely on technical advancements.

Genetic architecture of post-harvest tuber quality traits in bush yam (Dioscorea praehensilis Benth.) germplasm through association mapping

IntroductionBush yam (Dioscorea praehensilis Benth.) is an important semi-domesticated food crop in West Africa. Limited information on the genetic architecture and its poor post-harvest tuber quality traits significantly hinder its use as food and source of income. Hence, dissecting the genetics underlying the expression of its post-harvest tuber quality traits is essential for establishing proper breeding schemes.MethodsIn this study, 138 D. praehensilis accessions collected in Ghana were sequenced using Diversity Array Technology (DArTSeq). The materials were profiled for dry matter content (DMC), tuber flesh oxidation (TBOXI) and for tuber flesh hardness (TBhard) during two cropping seasons.Results and discussionDiversity assessment using population structure, principal component analysis and hierarchical clustering methods revealed the presence of three major groups. Six genetic models were used for the trait association analysis using multiple random locus mixed linear model (MrMLM). Sixteen SNP markers distributed across the yam genome were identified to be associated with the evaluated traits. The associated SNP markers displayed a phenotypic variance ranged from 4.22% in TBHard to 16.92% in TBOXI. A total 25 putative candidate genes were identified around the SNP markers. The putative genes were identified to play key roles in tuber bulking, oxidative browning and starch hydroxylase. This study provides a valuable insight on the genetics underlying tuber quality traits in bush yam and opens avenues for developing genomic resources to improve D. praehensilis.

Ensemble of Domain Adaptation-Based Knowledge Transfer for Evolutionary Multitasking

Recently, a number of domain adaptation methods have been proposed for knowledge transfer in evolutionary multitasking (EMT). However, the learnt mappings in these methods often have unique biases in representing the connection between source and target tasks. Few studies have paid attention to the complementarity of different mappings in knowledge transfer. To fill this research gap, this paper proposes an ensemble method to combine multiple domain adaptation methods for knowledge transfer in EMT by considering the efficacy and diversity of these methods. First, a hierarchical clustering method is used to divide the population of each task into multiple clusters. Then, when two parental solutions are selected for knowledge transfer across tasks, the solutions within the same cluster are checked. In particular, if none of these solutions has been transferred before, the efficacy of domain adaptation methods is considered first by using roulette wheel selection based on the corresponding performance improvements in the evolutionary optimization process. Otherwise, the diversity of domain adaptation methods is emphasized by randomly selecting one of the domain adaptation methods for knowledge transfer. The effectiveness of our proposed ensemble method is validated by embedding it into existing state-of-the-art EMT algorithms, and the experimental results show that our algorithm outperforms several recently proposed EMT algorithms on most cases of two multitasking benchmark suites and one practical case.

Comparative Evaluation of Six Agglomerative Hierarchical Clustering Methods With a Robust Example

The agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods are represented by a bottom-up approach where each object starts in its cluster and pairs of clusters are merged as it moves up the hierarchy. In this paper, we present six agglomerative hierarchical clustering methods namely: the single linkage method, complete linkage method, average linkage method, centroid method, median method, and Ward’s method. We also evaluated how these methods work on a practical basis using a matrix of distance pairs of five points. It was observed that the single linkage method through its dendrogram produced the most similarity measure between x_i and x_j, while Ward’s method produced the highest distance measure between x_i and x_j.

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.

Towards customized mitigation strategy in the transportation sector: An integrated analysis framework combining LMDI and hierarchical clustering method

As the world's largest CO2 emitter, China's decarbonization in the transportation sector is vital for achieving global sustainable goals. This study develops an in-depth framework to elucidate and tailor decarbonization strategies across regions effectively. Through the application of the Logarithmic Mean Divisia Index (LMDI) approach, this analysis spans transportation, energy, economic, and demographic factors influencing CO2 emissions in China's 30 provinces over the decade 2010–2019. The Entropy Weight Method (EWM), applied innovatively, pinpoints regional disparities in emission drivers. A dual-dimensional hierarchical clustering analysis evaluates the emission characteristics at provincial-level, laying the groundwork for customized mitigation strategies. The study delineates three principal discoveries: (1) robust economic development has spurred transportation demand over the past decade, escalating emissions. While initiatives like the "Beautiful China" campaign and decarbonization policies have fostered weak decoupling between economic growth and emissions, economic activity remains a dominant driver of emissions at the provincial level. (2) The energy intensity, freight intensity, and industrial structure are the primary sources of regional disparities in the driving mechanism of CO2 emissions in the transportation sector. In economically developed areas, freight intensity is a considerable emissions accelerator, marking a departure from its national-level emissions reduction role. Given the starkly distinct regional disparity, a “one-size-fits-all” approach for China would be imprudent. (3) The novel application of hierarchical clustering reveals three synergistic relationships between regional development and transportation decarbonization, highlighting the misalignment between emissions reduction potential and driving forces in 27 provinces. This misalignment underscores the inefficiency of current management practices, necessitating the development of precise tailored strategies. By pioneering in the methodological analysis of emissions dynamics and stressing the imperative of customized mitigation strategies, this research significantly enriches the discourse on sustainable transportation and regional policy innovation.

Multi-platform omics sequencing dissects the atlas of plasma-derived exosomes in rats with or without depression-like behavior after traumatic spinal cord injury

BackgroundExosomes can penetrate the blood-brain barrier for material exchange between the peripheral and central nervous systems. Differences in exosome contents could explain the susceptibility of different individuals to depression-like behavior after traumatic spinal cord injury (TSCI). MethodsHierarchical clustering was used to integrate multiple depression-related behavioral outcomes in sham and TSCI rats and ultimately identify non-depressed and depressed rats. The difference in plasma exosome contents between non-depressed and depressed rats after TSCI was assessed in 15 random subjects by performing plasma exosome transcriptomics, mass spectroscope-based proteomics, and non-targeted metabolomics analyses. ResultsThe results revealed that about 27.6% of the rats developed depression-like behavior after TSCI. Totally, 10 differential metabolites, 81 differentially expressed proteins (DEPs), 373 differentially expressed genes (DEGs), and 55 differentially expressed miRNAs (DEmiRNAs) were identified between non-depressed TSCI and sham rats. Meanwhile, 37 differential metabolites, 499 DEPs, 1361 DEGs, and 89 DEmiRNAs were identified between depressed and non-depressed TSCI rats. Enrichment analysis showed that the progression of depression-like behavior after TSCI may be related to amino acid metabolism disorder and dysfunction of multiple signaling pathways, including endocytosis, lipid and atherosclerosis, toll−like receptor, TNF, and PI3K-Akt pathway. ConclusionOverall, our study systematically revealed for the first time the differences in plasma exosome contents between non-depressed and depressed rats after TSCI, which will help broaden our understanding of the complex molecular mechanisms involved in brain functional recombination after TSCI.

The Spatiotemporal Clustering of Short‐Duration Rainstorms in Shanghai City Using a Sub‐Hourly Gauge Network

AbstractImpacted by both the large‐scale climate change and the local hydrometeorological atmosphere, the spatiotemporal structure and properties of short‐duration extreme rainfall events in urban areas remain a complex issue. This study explores the spatiotemporal pattern variability of short‐duration rainstorm events using an exceptional sub‐hourly gauge network in Shanghai, China. A catalog of 207 “independent” rainstorms is extracted. The hierarchical clustering method is used to examine the spatial and temporal features including rainfall duration, intensity, and coverage for each district with different degrees of urbanization in the 2014–2018 period. The results point to a pronounced spatial heterogeneity with heavier rainfall magnitude in the north. In the urban core area, intensive short‐duration rainstorms hit more frequently than in other districts. Rainstorms can be divided into three types from the spatiotemporal process: Type delayed‐slow, Type advanced‐fast, and Type advanced‐slow. The delayed‐peak with a higher slope occurs more in the west and the advanced‐peak more along the coastal line. The other type of advanced‐peak storm with a reverse trend of intensity and coverage concentrates in June–August of the year and the 16:00–20:00 of the day. While 65.6% of the delayed‐peak storms only occur in one district, more than half of the advanced‐peak appear without other types at the same time. These findings show the importance of observation‐based analysis in understanding the spatiotemporal pattern of short‐duration storms and provide a reference for storm design of sub‐regions in urban areas.

A New Approach for Discontinuity Extraction Based on an Improved Naive Bayes Classifier

An increasing number of methods are being used to extract rock discontinuities from 3D point cloud data of rock surfaces. In this paper, a new method for automatic extraction of rock discontinuity based on an improved Naive Bayes classifier is proposed. The method first uses principal component analysis to find the normal vectors of the points, and then generates a certain number of random point sets around the selected training points for training the classifier. The trained, improved Naive Bayes classifier is based on point normal vectors and is able to automatically remove noise points due to various reasons in conjunction with the knee point algorithm, realizing high-precision extraction of the discontinuity sets. Subsequently, the individual discontinuities are segmented using a hierarchical density-based spatial clustering method with noise application. Finally, the PCA algorithm is used to complete the orientation by plane fitting the individual discontinuities. The method was applied in two cases, Kingston and Colorado, and the reliability and advantages of the new method were verified by comparing the results with those of previous research, and the discussion and analysis determined the optimal values of the relevant parameters in the algorithm.

Voltage angle‐based coherency identification in power system

AbstractCoherency detection in power system is vital step for controlled‐islanding. Model‐based slow‐coherency method is the traditional method of detecting coherent areas, which suffers from computational burdens. With the implementation of a wide‐area measurement system (WAMS) in power systems, coherent areas can be detected early after a disturbance that helps to ensure quick separation or remedial action. In this study, a novel Synchrophasor‐based coherency identification method has been proposed with bus voltage angle measurement. A new algorithm is proposed based on the first and second derivatives of the bus voltage phase angle using hierarchical clustering method for coherency detection. The proposed approach leverages hierarchical clustering method and the dynamics reflected in the bus voltage phase angle behavior to achieve automatic and accurate coherency identification. Real time simulation results demonstrate the ability of the proposed algorithm to detect the coherency with three consecutive PMU data from all the system buses and showcase coherent area boundaries. Case studies show robustness of the proposed method and is least affected by the latency in communication network compared to conventional methods. The proposed algorithm has been mathematically formulated and evaluated with different benchmark systems in OPAL‐RT real‐time simulation environment with HYPERSIM platform.

Impact of Dataset Scaling on Hierarchical Clustering: A Comparative Analysis of Distance-Based and Ratio-Based Methods

In this study, the distance-based agglomerative hierarchical clustering techniques were compared to a ratio-based approach. Two real datasets, which were also used in a prior study by Roux (2018), were considered. Firstly, it was observed that the type of scaling applied to the datasets was found to affect the results of hierarchical clustering. Thus, various scaling methods were employed prior to implementing hierarchical clustering. Furthermore, two rank-based goodness-of-fit measures were used to evaluate the hierarchical clustering methods. In contrast to Roux (2018) findings, it was observed that the distance-based methods, such as Median linkage, Average linkage, and centroid linkage, performed better than the ratio-based method. The ratio-based methods also showed issues with branch crossing in the hierarchical clustering dendrogram. Consequently, this study illustrates that, with appropriate dataset scaling, the distance-based methods outperform ratio-based methods in terms of goodness-of-fit measures.

Storm in a teacup: implications of mobile phone literacy on sustainable smallholder agri-food supply chains in developing economies

PurposeThis study explores digital transformation in the tea supply chain within developing economies, with a focus on smallholder tea producers in Sri Lanka. Tea is one of the most widely consumed beverages in the world. Among the tea producers, smallholder tea producers account for a substantial portion of total tea production in several countries. Mobile phones play a significant role in providing smallholder producers with access to crucial agricultural information, markets and financial services.Design/methodology/approachThis study adopts a deductive approach, analysing mobile phone ownership, literacy, experience and perception among smallholder tea producers. The chi-squared test of independence and hierarchical clustering methods were used to test the hypotheses and address the research questions.FindingsThe study identifies four clusters of smallholder tea producers as Basic Tech Adopters, Digital Laggards, Skeptical Feature Phone Users and Tech-savvy Adopters based on their characteristics towards mobile-based technologies. Approximately 75% of the surveyed sample, which included both tech-savvy and basic-tech adopters, showed a positive attitude toward adopting mobile-based agricultural technologies.Practical implicationsThe study suggests developing targeted strategies and policies to enhance the productivity of the smallholder tea production process in developing economies. The study highlights the importance of awareness, access, affordability and availability when implementing digital services for businesses at the base of the pyramid, such as tea smallholdings in developing economies.Originality/valueThe present study aims to address the lack of data-driven empirical studies on the use of mobile phones in smallholder settings. The findings of this study enable the enhancement of entrepreneurship within the tea production supply chain, especially, within stakeholders who deliver digital transformation support services.

Machine learning-based characterisation of urban morphology with the street pattern

Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns.

Strategy for improving and empowering MSMEs through grouping using the AHC method

The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1]  MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Craft MSME data. The silhouette score test results in the Culinary Sector are in the strong structure category with a value of 0.79 and in the Crafts Sector are in the Medium Structure category with a value of 0.615. From the number of groups in the two MSMEs, strategies were obtained to improve and empower MSMEs, especially those with a turnover of less than 10 million, marketing objectives within the Yogyakarta area, and not having capital assistance from the government.  [a1]the result of the revision of the Abstract

Research on Clustering of Material Inspection Data for Highway Construction Projects Based on Agglomerative Hierarchical Clustering

In large-scale highway construction projects, the project implementers will carry out detailed testing of various material indicators used in the project. By processing and analyzing these material testing data, we can reasonably classify large-scale highway construction projects, so that each project implementer can better manage the quality of the materials, which will greatly help all parties to strengthen the quality control of the project and improve the level of project management. This study first preprocesses the material testing data to obtain a structured data set suitable for data analysis. Then statistical features are constructed for the features in the dataset, including maximum, minimum, mean, median and standard deviation, to improve the performance and accuracy of the model. Next, the clustering hierarchical clustering method is applied for classification and the classification results are visualized in the form of dendrograms. Finally, through the comparison of performance analysis and clustering evaluation indexes, it is concluded that the classification of works into 3 or 4 categories according to material performance is in line with the actual level of engineering quality.

Research on Vegetable Pricing and Replenishment Strategies Based on ARMA Models

This paper explores the importance of pricing and replenishment issues of vegetable items for merchant operations. First, data related to vegetable stocking were systematically collected to assess the correlation between different categories. Through Q-Q plot analysis, it was determined that the data did not conform to a normal distribution, so the Spearman correlation coefficient was chosen for further quantitative analysis. Considering the large number of individual vegetable categories, in order to deal with the correlation between different individual items, a hierarchical clustering method was introduced in this paper, which classified them into three categories: low volume, medium volume and high volume. In order to predict the future vegetable intake, this study adopted the seasonal ARMA model and applied the Pearson's correlation coefficient for correlation analysis by combining the cost utilization rate as well as the total sales volume. In addition, the relationship between sales and cost margin was fitted with the help of support vector machine. Finally, this study constructs a single optimization model with the objective of maximizing the revenue of the superstore. The innovation of this paper is the combination of various statistical and machine learning methods in order to solve the pricing and replenishment problem of vegetable items, aiming to improve the efficiency of merchant operations.