Average Linkage Research Articles

Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method

Mapping of Food Crop Commodity Production Areas in Indonesia Using The Average Linkage Method

AVERAGE LINKAGE CLUSTERING METHOD AND MOLECULAR DOCKING STUDY ON DATE PALM (PHOENIX DACTYLIFERA L.) AS POTENTIAL ANTI-ANEMIA AGENT

Anemia, characterized by blood hemoglobin (Hb) levels below the World Health Organization's (WHO) normal limit, remains a significant health concern. Date fruit (Phoenix dactylifera L.) stands out as an herbal plant boasting the highest iron content at 13.7 mg, suggesting its potential as an anti-anemia agent. This study aimed to explore the anti-anemia potential of active compounds in date fruit using average linkage clustering and validated using molecular docking. Compounds from dates were gathered via GC-MS analysis and online databases, totaling 145 compounds—50 from GC-MS and 95 from Knapsack and Dr. Duke databases. Additionally, 5 lead compounds served as positive controls for comparison. SwissADME online servers assessed the compounds' properties, serving as materials for the clustering method. The average linkage clustering method was employed, yielding an excellent dendrogram with a cophenetic correlation of 0.711. Notably, a total of 17 date fruit compounds are in the same cluster as the lead compounds. Molecular docking revealed 4 date palm fruit-derived compounds as potential PHD enzyme inhibitors, promising for anemia treatment. In conclusion, the average linkage clustering method and validation using molecular docking approaches highlight date fruit's potential as an alternative anemia treatment, showcasing the significance of interdisciplinary methodologies in drug discovery.

Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system

The reconfigurable manufacturing system (RMS) is an advanced manufacturing strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations to be adapted for new part requirements. Optimizing flow line design to produce various parts involves minimizing reconfigurations and associated costs by enhancing operation sequence similarity. This article proposes a novel sequence optimization using the Longest Common Subsequence (LCS) method to reduce bypassing moves and machine idle times. The study introduces a similarity coefficient derived from LCS and employs average linkage hierarchical clustering to categorize parts in a case study. Unlike traditional methods, this approach considers material movements both before the initial machine and after the final processing station, addressing gaps in bypassing move calculations. The impact of different weighting scenarios for Type-II moves (ω) and machine idleness (β) on clustering was examined. For example, with Type-II move weights (ω) of {1.0, 0.6, 0.3, 0.0} and equal weightings for bypassing moves (α) and machine idleness (β) set at 0.5, a threshold value of 0.3 results in eight clusters, such as Cluster 1 {1, 11, 10, 12} and Cluster 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower threshold values lead to fewer clusters with larger sizes, indicating a more consolidated part family grouping. Various Type-II move weights and material handling scenarios demonstrate how different weighting configurations affect clustering and part family sizes. This approach enhances RMS efficiency by integrating comprehensive material handling considerations and optimizing clustering based on operational similarities.

Clustering of Eligibility and Characteristics of Smart Indonesia Card Recipients for College using Agglomerative Hierarchical Clustering

Since 2023, the Smart Indonesia Card or Kartu Indonesia Pintar-Kuliah (KIP-K) has two schemes. Consisting of KIP-K Scheme 1, where scholarship recipients are exempted from tuition fees and receive living allowances during the KIP-K scholarship period and KIP-K Scheme 2, where scholarship recipients are only exempted from tuition fees without receiving living allowances. Lack of information about the characteristics of prospective KIP-K scholarship recipients is a problem in itself. Many universities/institutions are not on target in providing this scholarship. This study aims to obtain clusters of recipients of KIP-K Scheme 1, KIP-K Scheme 2, and Not Eligible who are received and to obtain the characteristics of recipients of KIP-K Scheme 1, KIP-K Scheme 2, and Not Eligible who are received. This research method uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach consisting of the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment and uses a combined method of Agglomerative Hierarchical Clustering with Average Linkage and uses the Calinski-Harabasz Index to test the validity of the cluster. The results of the study showed that the best cluster to explore was in 10 clusters with a Calinski-Harabasz Index value of 34.88236681148884. It was concluded that the feasibility of Scheme 1 KIP-K in cluster 8, 9 and 10.

Spatial-Temporal Visualization of Dengue Fever Vulnerability in Kediri Using Hierarchy Clustering Based on Mobile Devices

In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.

Comparisons of Different Cluster Analysis Methods with Application to Mizoram Rainfall Data

Analysing rainfall patterns in Mizoram from 1998 to 2017 reveals diverse trends. The highest average rainfall occurred in 2004, reaching 292.8 mm, while 2014 marked the lowest at 151.77 mm. Siaha District experienced the highest average rainfall (3020.2 mm), while Champhai District had the lowest (1663 mm). In 2017, the Kendall method showed correlations between temperature and relative humidity, rainfall and relative humidity, but not between rainfall and temperature. Cluster analysis, a technique partitioning datasets into cohesive groups, was applied to Mizoram’s district-wise rainfall data using single, complete, and average linkage methods. The Single Linkage Method formed one large cluster with under 26% similarity and the shortest distances between data points. The complete linkage method divided districts into two clusters with under 26% similarity and maximal inter-cluster distance. The Average Linkage Method merged all districts into one cluster with under 26% similarity and minimised inter-cluster distances. Comparing the techniques, Single and Complete Linkage Methods proved most effective for Mizoram’s district-wise rainfall data. With only eight districts, forming additional clusters remained limited. This analysis highlights the significance of rainfall patterns in agricultural ecosystems and the utility of statistical methodologies like cluster analysis in understanding long-term trends.

Volatile and non-volatile metabolite profiling of under-explored crop Piper colubrinum link. With chromatography mass spectrometry approach and its biochemical diversity from medicinally valued Piper species

Piper colubrinum Link. is an underexplored crop regarding its metabolites and therapeutic attributes. Current study aimed to identify the possible volatile and non-volatile metabolites of P. colubrinum fruit and studied its metabolite diversity with medicinally valued Piper species viz. P. nigrum L., P. longum L. and P. chaba Hunter. The volatile constituents of P. colubrinum essential oil by GC–MS revealed the presence of sesquiterpenes as the major contribution. The sesquiterpenes α-muurolol (12.5 %) and β-caryophyllene (11.3 %) were the predominant volatile components. Few aliphatic compounds like n-heptadecane and trace amounts of monoterpenes (α- and β-pinene and α-terpineol) were also identified from this crop. The fatty acid profiling by GC–MS revealed mainly oleic acid (41.3 %) followed by palmitic and linoleic acids. HPLC analysis demonstrated that the major pungent alkaloid piperine was found to be trace (0.04 %) in P. colubrinum. The LC-QTOF-MS/MS profiling of the chloroform extract of the P. colubrinum revealed the presence of non-volatile constituents including phenolic and alkaloid compounds. Ferulic acid, rosmarinic acid, salicylic acid, kaempferol-5-glucoside, 5-methoxysalicylic acid, apigenin-7-galactoside, kaempferide-3-glucoside, luteolin, kaempferol, apigenin and scutellarein-4′-methyl ether were the phenolic compounds whereas piperlonguminine was the alkaloid compound identified. Finally, the biochemical parameters of this crop were compared with that of P. nigrum, P. longum and P. chaba and average linkage cluster dendrogram revealed that P. colubrinum was biochemically distinct from other three Piper species.

Pengelompokan Produksi Padi dan Beras Provinsi Jawa Timur dengan Metode Agglomerative Hierarchical Clustering

Paddy is a food crop that produces rice which is widely consumed for basic needs and has become a source of income for the Indonesian people. Often the need for rice continues to increase and cannot only rely on domestic production, so it is necessary to optimize paddy producing areas. Grouping rice-producing areas needs to be done to identify production potentials and areas that require improvement. This research aims to group paddy and rice production based on districts/cities in East Java Province. The method used is Agglomerative Hierarchical Clustering (AHC) with the best algorithm, namely average linkage based on the highest cophenetic correlation of 0.8478. The research results showed 4 clusters: cluster 1 (high) with 4 districts, cluster 2 (low) with 18 districts, cluster 3 (medium) with 7 districts, and cluster 4 (very low) with 9 cities. The silhouette coefficient test is 0.6347, close to 1, thus indicating good clustering. These results can be used as a reference for the government and society in optimizing paddy and rice production to maintain the country's food security

Genome-wide association study identifies loci and candidate genes for RVA parameters in wheat (Triticum aestivum L.).

The gelatinization and retrogradation characteristics of wheat starch affect the eating quality of Chinese-style food. Rapid Visco Analyzer (RVA) parameters have been widely used as important indicators to evaluate and improve the quality of wheat starch. However, the genetic basis of RVA parameters remains to be further explored. In the present study, a natural population was genotyped using 90K single nucleotide polymorphism (SNP) arrays, and the RVA parameters of this population grown in five environments were evaluated. The results showed that 22,068 high-quality SNP markers were identified and distributed unequally on the chromosomes. According to the genetic distance, 214 wheat materials were divided into four groups. Except for the pasting temperature (PTT), six parameters followed a normal distribution. Based on the general linear model, 969 significant association SNPs were detected by genome-wide association studies (GWAS), and chromosomes 7A and 2B had the most associated SNPs. Breakdown viscosity (BV) was associated with the most SNPs (n = 238), followed by PTT (n = 186), peak viscosity (PV; n = 156), trough viscosity (TV; n = 127), and final viscosity (FV; n = 126). According to the average linkage disequilibrium (LD), 33 stable quantitative trait loci (QTLs) were identified for single parameters in multiple environments, of which 12 were associated with BV, followed by peak time (PT; n = 8) and PTT (n = 7). On the other hand, 67 pleiotropic QTLs were identified for multiple parameters. Three candidate genes-TasbeIIa, TasbeI, and TassIIa-were screened for phenotyping analysis. The grain width and the weight of the TasbeIIa and TaSSIIa knockout (KO) lines were significantly lower than those of the TasbeI KO lines and the control (CK). The KO lines had smaller endosperm cells, smaller A-type starch granules, and higher amylose content. The TasbeI KO lines showed normal RVA curves, while the TasbeIIa KO lines showed flat curves. However, the TaSSIIa lines failed to paste under the RVA temperatures. Conclusively, the SNPs/QTLs significantly associated with the RVA parameters and genetic resources with novel haplotypes could be used to improve the quality of wheat starch.

Peranan Sektor Industri Pengolahan Dalam Perekonomian di Indonesia dengan Pendekatan Input Output Tahun 2010-2016

Abstrak In the analysis of the Indonesian input output table, it can be seen that the processing sector has a quite important role in the economy in Indonesia. From the linkage analysis, the sectors that have above average future linkage figures are the electricity, gas and industrial sectors. However, from the results of the distribution analysis, divided into two, namely the distribution coefficient and distribution sensitivity, it was concluded that the processing industry sector had a distribution coefficient value of 1,090 and a distribution sensitivity value of 3,143. And the results of the multiplier analysis of the output of the processing industry sector were only able to reach third place, namely 1,815 from the classification of 9 sectors of the Indonesian economy.

Key gene modules and hub genes associated with pyrethroid and organophosphate resistance in Anopheles mosquitoes: a systems biology approach

Indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are the main methods used to control mosquito populations for malaria prevention. The efficacy of these strategies is threatened by the spread of insecticide resistance (IR), limiting the success of malaria control. Studies of the genetic evolution leading to insecticide resistance could enable the identification of molecular markers that can be used for IR surveillance and an improved understanding of the molecular mechanisms associated with IR. This study used a weighted gene co-expression network analysis (WGCNA) algorithm, a systems biology approach, to identify genes with similar co-expression patterns (modules) and hub genes that are potential molecular markers for insecticide resistance surveillance in Kenya and Benin. A total of 20 and 26 gene co-expression modules were identified via average linkage hierarchical clustering from Anopheles arabiensis and An. gambiae, respectively, and hub genes (highly connected genes) were identified within each module. Three specific genes stood out: serine protease, E3 ubiquitin-protein ligase, and cuticular proteins, which were top hub genes in both species and could serve as potential markers and targets for monitoring IR in these malaria vectors. In addition to the identified markers, we explored molecular mechanisms using enrichment maps that revealed a complex process involving multiple steps, from odorant binding and neuronal signaling to cellular responses, immune modulation, cellular metabolism, and gene regulation. Incorporation of these dynamics into the development of new insecticides and the tracking of insecticide resistance could improve the sustainable and cost-effective deployment of interventions.

Phenotypic analysis of mezcal agaves from the Central Valleys of Oaxaca

Objective: Mezcal agaves in the state of Oaxaca have a high economic value due to the demand for mezcal production; therefore, the purpose of this work was to assess and highlight the importance of morphological diversity within and among cultivated Agave species. Design/Methodology/Approach: A completely randomized experimental design with 25 treatments (populations) and 11 replicates (individuals) was implemented. The plant (individual) was the experimental unit and 19 morphological descriptors proposed by the National Seed Inspection and Certification Service (SNICS) were assessed. A total of 275 individuals of the Agave angustifolia, Agave karwinskii, Agave marmorata, Agave rhodacantha, Agave potatorum, Agave seemanniana, and Agave nussaviorum species were assessed using a multivariate analysis to determine their phenotypic variability and existing relationships. Results: The dendrograms for the Q-mode and R-mode were obtained by means of a cluster analysis, forming 4 groups based on the average linkage generated from the standardized BDM of the sampled species of the genus Agave. Four groups were formed using the k-means clustering method, in accordance with field observations and a review of the taxonomic bibliography. The first two principal components (PC) accounted for 66.4% of the total variation, according to the principal component analysis (PCA). For PC1 and PC2, the variables with the highest contribution were those related to leaf shape (Fh), size of the lateral spine (FEL), number of leaves (Nh), plant height (H), uniformity in the size of the lateral spines (UTE), and terminal spine shape (FET). Study Limitations/Implications: A comprehensive study requires taxonomic keys to identify species, subspecies, and even varieties of Agave. Additionally, molecular characterization is essential to understand the variability and phylogenetic relationships of these populations, subject to a phylogenetic analysis. Findings/Conclusions: Multivariate analysis techniques revealed that three species showed high phenotypic variability in the maturation stage under cultivation conditions. The A. potatorum and A. karwinskii species had a greater intra-population phenotypic variability, with significant differences within the same species. Agave marmorata showed no intra- or inter-population variability. Leaf texture (Txh) was the only variable that explained the variation within its group. This is a tall species whose diameter is larger than in the other species. The variables of the Mexican (Agave rhodacantha) group showed low correlation, as their behavior was highly dispersed. The variables obtained in the field from this group of populations must be meticulously assessed to identify the degree of correlation between the variables and to confirm the behavior of this group.

Integrating multilocus genome-wide association studies in chickpea landraces to discern the genetics of drought tolerance

In chickpea breeding, drought is a major concern and a complex trait controlled by several genes. To develop drought-tolerant varieties, it is essential to use the available germplasm and genomic resources. Over the years, the landraces have proven to be a good source for the dissection of genes for different yield and yield-related traits. The present investigation for marker–trait associations (MTAs) and candidate gene identification was conducted by studying 125 chickpea landraces collected from the West Asia and North Africa (WANA) region, along with 4 varieties suitable for irrigated and rainfed environments. This study analyzed 13 physio-morphological traits in 2 consecutive years at two isolated locations (IARI, New Delhi, and Dharwad). A strong correlation coefficient was observed between the trait seed yield (SY) and biological yield (BY) under both conditions. The Drought Susceptibility Index (DSI) ranged from 0.02 to 1.84 and 0.10 to 2.04 at the IARI, New Delhi and Dharwad locations, respectively. The genotypic data of 6,367 single nucleotide polymorphisms (SNPs) distributed across the genome were used for genetic diversity study, population structure, and genome-wide association study (GWAS). The average polymorphic information content (PIC) value observed was 0.25, and the average linkage disequilibrium (LD) decay distance was 152,269 bp across the genome. A total of four subgroups were observed within the population for genotypic data. Fixed and random model Circulating Probability Unification (FarmCPU) was used for the GWAS analysis, which considered both fixed- and random-effect models. A total of 52 significant SNPs were reported in both irrigated and rainfed conditions at low locations; 7 SNPs were associated with more than one trait, which may have pleiotropic effects. Significant SNPs were annotated in the pulse database. The identified genomic region found in or near MTA under rainfed conditions encodes for guard cell hydrogen peroxide-resistant1 (GHR1), late embryogenesis-abundant, E3 ubiquitin-protein ligase, walls are thin1 (WAT1), and beta-galactosidase that are known to be associated with drought tolerance.

Klasterisasi Kabupaten/Kota di Provinsi Papua Berdasarkan Indikator Pembangunan Manusia Tahun 2022

The Human Development Index (HDI) is a human development metric empowered by several main elements that reflect the quality of life, namely HLS, RLS, PKK, and UHH. High HDI is one of the basic capital for a country's sustainable development. The improvement of human development in Indonesia continues to increase every year. However, there is still uneven development between regions in Indonesia. The HDI of Papua Province has always been the lowest in Indonesia in the last 10 years and each indicator is still unevenly distributed between districts/cities. This needs to be a special concern by the government. Thus, this study was carried out with the aim of grouping districts / cities in Papua Province based on human development indicators with the application of the agglomerative hierarchy cluster method. Based on the highest correlation value, Average Linkage is the best method with the optimal number of clusters formed is five clusters (Very High, High, Medium, Low, and Very Low) according to the Calinski-Harabasz Index test conducted. The findings of this study can be the basis for designing equitable development policies in districts/cities in Papua Province.

ANALYSIS OF RAINFALL IN INDONESIA USING A TIME SERIES-BASED CLUSTERING APPROACH

Indonesia has a tropical climate and has two seasons: dry and rainy. Prolonged drought can cause drought disasters, and rain can cause floods and landslides. According to information from the Meteorology, Climatology, and Geophysics Agency (BMKG), natural disasters such as floods and landslides due to heavy rains have been a severe problem in Indonesia for the past five years. Different regional characteristics can affect the intensity of rain that falls in every province in Indonesia. It can be grouped to determine which provinces have similar characteristics to natural disasters due to rainfall. Later, it can provide information to the government and the public so that they are more aware of natural disasters. So, it is necessary to research and classify provinces in Indonesia for rainfall with cluster analysis. The data used is secondary rainfall data taken from the official BMKG website. Cluster analysis of rainfall in 34 provinces in Indonesia used hierarchical and non-hierarchical methods in this study. The approach that is used in this research limits our clustering of the data. Further research with a machine learning approach is recommended. For the clustering method, the agglomerative hierarchical method includes single, average, and complete linkage. The non-hierarchical method includes k-medoids and fuzzy c-means. The cluster analysis results show that the dynamic time warping (DTW) distance measurement method with the average linkage method has the most optimal cluster results with a silhouette coefficient value of 0.813.

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.

Genome identification and diversity analysis of banana (Musa spp.) from Jember based on morphological characters

Introduction: Banana plants are easy to find and grow in Indonesia, including in Jember. Banana production in Jember in 2020 increased by 50% and has the potential to be inventoried and developed. Information regarding the characteristics of various banana cultivars is needed for future banana breeding. This research was conducted to identify and classify the genome of bananas growing in Jember and determine the level of banana diversity. Methods: The banana exploration using a purposive sampling method. The observation of 15 morphological characters followed the method of Simmonds and Shepherd. Determination of the genome followed the method of Silayoi & Chamchalow and Singh & Uma. The kinship analysis uses cluster analysis using the average linkage method and Gower distance, by R Studio software. Results: There were 23 banana cultivars obtained in this research, consisting of 35% dessert bananas and 65% plantain types. Genome identification results in four genome groups, namely AA/AAA, AAB, ABB, and BB. Cluster analysis produces three main groups, where its branch is generally consistent with the genome grouping. The closest relationship is between Rayap and Seribu bananas (0.96), while the furthest is between Gaje and Agung bananas (0.38). The dissimilarity value for 23 banana cultivars is 0.36, or the similarity is 0.64. Conclusion: The 23 banana cultivars are closely related or have low diversity based on the 15 morphological characters observed. Further identification can be carried out with a greater number of morphological characters, accompanied by molecular identification.

Clustering analysis for the evolutionary relationships of SARS-CoV-2 strains.

To explore the differences and relationships between the available SARS-CoV-2 strains and predict the potential evolutionary direction of these strains, we employ the hierarchical clustering analysis to investigate the evolutionary relationships between the SARS-CoV-2 strains utilizing the genomic sequences collected in China till January 7, 2023. We encode the sequences of the existing SARS-CoV-2 strains into numerical data through k-mer algorithm, then propose four methods to select the representative sample from each type of strains to comprise the dataset for clustering analysis. Three hierarchical clustering algorithms named Ward-Euclidean, Ward-Jaccard, and Average-Euclidean are introduced through combing the Euclidean and Jaccard distance with the Ward and Average linkage clustering algorithms embedded in the OriginPro software. Experimental results reveal that BF.28, BE.1.1.1, BA.5.3, and BA.5.6.4 strains exhibit distinct characteristics which are not observed in other types of SARS-CoV-2 strains, suggesting their being the majority potential sources which the future SARS-CoV-2 strains' evolution from. Moreover, BA.2.75, CH.1.1, BA.2, BA.5.1.3, BF.7, and B.1.1.214 strains demonstrate enhanced abilities in terms of immune evasion, transmissibility, and pathogenicity. Hence, closely monitoring the evolutionary trends of these strains is crucial to mitigate their impact on public health and society as far as possible.

K-MEANS AND AGGLOMERATIVE HIERARCHY CLUSTERING ANALYSIS ON THE STAINLESS STEEL CORROSION PROBLEM

Stainless Steel (SS) is a material that is widely used in various fields because it is resistant to corrosion. However, if SS is exposed to heat at high temperatures for a long period of time, a sigma phase, namely the Fe-Cr compound, will form, which indicates that corrosion has begun. The appearance of this corrosion can be detected through color changes on the SS surface, ranging from light brown to dark blue. Corrosion events will be observed through the distribution of color on the sample surface at the location selected through the SS microstructure image. Cluster analysis will be used to group the colors on the surface of the SS sample through the images used. The results of cluster analysis can be used to identify SS color which indicates the appearance of corrosion in the sample. In this research, we will examine the determination of many clusters for K-Means and Agglomerative Hierarchy with Ward's Criterion, Single, Average, and Complete Linkages. In addition, the model quality measure was tested with Silhouette Coeficient. Single linkage gives the worst results because it gives the impression that only one dominant color appears so it can be said that it is unable to distribute each color to the specified cluster. Likewise with Average because the number of clusters cannot be determined with certainty. On the other hand, the K-Means results are similar to Ward's results, this is reasonable because the basic idea of both is to find the minimum distance between each object and its center, in this case the average is used as the measure of the center, while the results that are most similar to the original image are clustering uses complete linkage. These results can be used as recommendations for academics and practitioners in the fields of Statistics, Mathematics and Materials Engineering in the subsequent analysis process to solve SS corrosion problems.

Comparison of Clustering Approaches in a Multimodel Ensemble for U.S. East Coast Cold Season Extratropical Cyclones

Abstract Several clustering approaches are evaluated for 1–9-day forecasts using a multimodel ensemble that includes the GEFS, ECMWF, and Canadian ensembles. Six clustering algorithms and three clustering spaces are evaluated using mean sea level pressure (MSLP) and 12-h accumulated precipitation (APCP) for cool-season extratropical cyclones across the Northeast United States. Using the MSLP cluster membership to obtain the APCP clusters is also evaluated, along with applying clustering determined at one lead time to cluster forecasts at a different lead time. Five scenarios from each clustering algorithm are evaluated using displacement and intensity/amount errors from the scenario nearest to the MSLP and 12-h APCP analyses in the NCEP GFS and ERA5, respectively. Most clustering strategies yield similar improvements over the full ensemble mean and are similar in probabilistic skill except that 1) intensity displacement space gives lower MSLP displacement and intensity errors; and 2) Euclidean space and agglomerative hierarchical clustering, when using either full or average linkage, struggle to produce reasonably sized clusters. Applying clusters derived from MSLP to 12-h APCP forecasts is not as skillful as clustering by 12-h APCP directly, especially if several members contain little precipitation. Use of the same cluster membership for one lead time to cluster the forecast at another lead time is less skillful than clustering independently at each forecast lead time. Finally, the number of members within each cluster does not necessarily correspond with the best forecast, especially at the longer lead times, when the probability of the smallest cluster being the best scenario was usually underestimated. Significance Statement Numerical weather prediction ensembles are widely used, but more postprocessing tools are necessary to help forecasters interpret and communicate the possible outcomes. This study evaluates various clustering approaches, combining a large number of model forecasts with similar attributes together into a small number of scenarios. The 1–9-day forecasts of both sea level pressure and 12-h precipitation are used to evaluate the clustering approaches for a large number of U.S. East Coast winter cyclones, which is an important forecast problem for this region.