Linkage Clustering Research Articles

Reconfigurable Manufacturing Systems: Enhancing Efficiency via Product Family Optimization

In response to the increasing complexity of modern production environments driven by heightened competition, customer demands, and sustainability goals, this work presents a focused methodology for forming product families within reconfigurable manufacturing systems (RMSs). Unlike traditional approaches, our method emphasizes the practical implementation of the Analytic Hierarchy Process (AHP) and the Average Linkage Clustering (ALC) algorithm to optimize RMS configurations. By evaluating specific comparison criteria—such as assembly sequence, machining sequence, components, production tools, and production demand—we aim to enhance resource utilization and adaptability to market changes. The proposed methodology enables a systematic assessment of RMS performance tailored to diverse product requirements. A detailed example of machining systems demonstrates the use of machining sequence and tool usage as primary criteria, showcasing the practical application and decision-making capabilities of the approach. This work contributes to the field by providing a structured framework for decision-making in RMS, facilitating efficient and precise product family formation to meet evolving manufacturing demands.

Chinese consumers’ dish value: a best–worst scaling approach

PurposeThis paper aims to identify Chinese consumers’ dish value systems and investigate whether and to what extent their dish values vary by type of consumers.Design/methodology/approachWe used a best–worst scaling (BWS) developed based on previous literature on food values and attributes of Chinese dishes to elicit Chinese consumers’ dish value structure. Then, we estimated each participant’s share of preferences for dish values and applied Ward’s linkage cluster analysis to identify the heterogeneity of their dish values.FindingsOur results indicate that food safety values, including the safety of agricultural products, additives and food processing, are valued most by Chinese consumers. They rate health-nutrition values as the second most important dish values. There is significant heterogeneity in Chinese consumers' dish values. Their dish values tend to drive their dish consumption patterns.Originality/valueThe dish is the mainstay of the Chinese meal. This study is among a few studies that examine Chinese consumers’ dish values that underlie consumer dish preferences and choices. The results provide essential information to promote a healthy and sustainable diet among Chinese consumers by understanding their values and motivations for making dish choices.

Forsyth-Edwards Notation in Chess Game Clustering: A Depth-Based Evaluation

Chess games clustering poses the challenge of accurately grouping games with similar strategies and positions, especially when the openings are similar. Previous research has used Portable Game Notation (PGN) as a feature for clustering, but its emphasis on move order can limit position transposition. This research addresses this limitation by evaluating Forsyth-Edwards Notation (FEN), which focuses on board position, as an alternative. Hierarchical clustering with complete linkage and K-means clustering were used to analyze 100 chess games at move depths of 20, 30, 40, and 60. Both methods effectively cluster games involving the English Opening and the Queen's Gambit Declined, with FEN providing slightly better differentiation than PGN. However, challenges remain in grouping French Defence variations, especially the Poulsen Attack and variations with 6.a3, due to positional similarities. This study underlines the robustness of FEN for clustering tasks and its compatibility with hierarchical clustering, highlighting the important role of move depth. The results provide a basis for refining clustering methods and using larger data sets to deepen insights into chess strategies.

National outbreak of Shiga toxin-producing Escherichia coli O145:H28 associated with pre-packed sandwiches, United Kingdom, May-June 2024.

Shiga toxin-producing Escherichia coli (STEC) is a group of bacteria that causes gastrointestinal illness and occasionally causes large foodborne outbreaks. It represents a major public health concern due to its ability to cause severe illness which can sometimes be fatal. This study was undertaken as part of a rapid investigation into a national foodborne outbreak of STEC O145. On 22 May 2024, United Kingdom (UK) public health agencies and laboratories identified an increase in stool specimens submissions and patients testing positive for Shiga toxin-producing E. coli (STEC). Whole genome sequencing (WGS) identified serotype O145:H28 stx2a/eae belonging to the same five single nucleotide polymorphism (SNP) single linkage cluster as the causative agent. By 3 July 2024, 288 cases had been linked to the cluster. Most cases were adults (87%) and females (57%), 49% were hospitalized with a further 10% attending emergency care. Descriptive epidemiology and analytical studies were conducted which identified consumption of nationally distributed pre-packed sandwiches as a common food exposure. The implicated food business operators voluntarily recalled ready-to-eat sandwiches and wraps containing lettuce on 14 June 2024.

Classification of Mandarin-speaking persons with aphasia: a discussion on validating the classification function of WAB-R

ABSTRACT Background The classical aphasia taxonomy has been incorporated into several widely utilised aphasia assessment tools, such as the Western Aphasia Battery (WAB). Validation of the WAB’s classification typically involves comparing it with statistical clustering analysis results, using overall agreement rates as the referential index. Nevertheless, previous studies have documented diverse agreement rates. The classification function of the Mandarin version of the revised WAB (WAB-R) has not been validated. Additionally, the discussion on validating classification function is inadequate. Aim The current study adopts the argument-based validation framework to validate the classification function of the Mandarin version of the WAB-R and discuss the efficacy of using cluster analysis as evidence for the validation. Methods & Procedures A validation argument for the classification function was developed, utilising the k-means cluster analysis and within-group linkage cluster analysis as statistical procedures. WAB-R testing score data were collected from 122 Mandarin-speaking persons with acquired aphasia (non-acute). Eight clusters were expected to be generated from statistical procedures as a confirmatory test. To explore the impact of cluster numbers, solutions of generating six and ten clusters were conducted for comparative analysis. The potential reasons for mismatches were analysed. A proposed modified fluency scale (raising the cutoff score from 4 to 5) was also tested on the current dataset. Outcomes & Results Under the solution of eight clusters, the overall agreement was 62.3% with the k-means cluster analysis and 56.6% with the within-group linkage cluster analysis. Agreement rates slightly increased with the number of clusters in the within-group linkage method and remained almost unchanged in the k-means method. Anomic aphasia presented the highest agreements, while the agreements of other subtypes varied across different solutions. The fundamental differences between the clustering algorithm and the rationale of the WAB-R classification criteria were the primary reason for mismatched cases, undermining the reliability of the validation procedure. The modified fluency scale was found to significantly improve the overall agreement rates. Conclusions The classification function of the WAB-R’s Mandarin adaptation was considered acceptable under the current method. The validation argument is suggested to be modified to enhance the rationale as the statistical cluster analysis showed reliability issues when being solely employed for validation. The mismatch rate did not form direct challenges to the validity of the classification function. Additionally, modifications to the classification criteria are suggested for further exploration as the modified fluency scale has demonstrated positive results.

Application of Two-Step Entropy–TOPSIS Method and Complete Linkage Clustering for Water-Pumping Windmill Investment on Thailand Peninsula

This study focuses on identifying suitable areas for the installation of water-pumping windmills in Thailand, which require wind speeds of at least 4 m/s to operate efficiently. A simple combined approach is introduced, integrating the Entropy–TOPSIS method complete linkage clustering to prioritize and categorize potential locations. Out of 271 initial areas, 28 have been selected based on their ability to meet the 4 m/s wind speed threshold. The Entropy–TOPSIS method first evaluates these areas based on monthly wind speed and agricultural area. The analysis reveals that regions with higher wind speeds generally score better for wind energy potential, while areas with larger agricultural spaces tend to score higher for farming suitability. The final integrated scores show that agricultural area is more significant, with a weight of 0.7788, compared to the wind speed weight of 0.2212. The areas are then ranked, and complete linkage clustering groups them into six categories, from the most to the least suitable for windmill installation. A sensitivity analysis confirms the robustness of the clustering method, as the group composition remains stable despite minor changes in weight adjustments. This approach simplifies decision-making for sustainable energy investments in Thailand agriculture sector.

SUSTAINABLE DEVELOPMENT PERFORMANCE OF COMPANIES: A CLUSTER ANALYSIS APPROACH

Sustainable development can be defined as an approach where firms support economic growth while reducing environmental impacts, fulfilling social responsibilities, and adhering to strong governance principles. This study investigates sustainable development performance of non-financial companies listed on Borsa Istanbul by cluster analysis of economic, environmental, social, and governance dimensions of corporate sustainable development. Hierarchical average linkage clustering and k-means clustering are used and both result in three distinct clusters. While the economic dimension shows no significant difference between clusters, significant variations are observed in the environmental, social, and governance dimensions. To understand the characteristics of these clusters further, a comparative analysis of financial ratios, company size, and company age is undertaken. Notably, company size emerges as the sole statistically significant differentiator between the clusters. The cluster with the highest environmental, social, and governance (ESG) scores comprises larger firms compared to the others. Based on the identified sustainable development patterns within each cluster, the study proposes recommendations for enhancing their overall sustainable development performance.

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.

Herpetofauna Diversity And Distribution Pattern Of Selected Ecological Sites Of Girei Local Government Area, Adamawa State – Nigeria

Background: This study aimed at understanding the diversity and distribution of herpetofauna resources with the specific objectives to: determine the relative abundance of herpetofauna resources; ascertain the distribution pattern of herpetofauna resources in relation to the ecological sites; and determine herpetofauna resource diversity in Girei Local Government Area of Adamawa State - Nigeria. Materials and Methods: The study area was divided into seven ecological sites: home area, open land area, farmland area, rocky area, grassland area, shrubby area and woodland area. Direct observation method involving an intensive search for the presence of herpetofauna resources was employed for the census. Cluster analysis was used to determine the pattern of distribution of herpetofauna resources while diversity indices were calculated using Simpson’s diversity index. Results: The results obtained showed that herpetofauna resources belonging to seven families were inventoried: Bufonidae, Ranidae, Agamidae, Lacertidae, Scincidae, Ophidae and Chamaeleonidae. The single –linkage cluster analysis showed only one cluster, indicating similar herpetofauna resources distribution in all the ecological sites. The Simpson’s diversity index of the herpetofauna resources was 0.2870. The results therefore suggest high diversity of herpetofauna resources, irrespective of ecological sites in the study area. It was concluded that there should be creation of herpetofauna refuge by encouraging people to establish hedgerows, fencerows, wood piles and preserve forest thickets between farmlands in the study area

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.

A Novel Connectivity Metric of Identified Multi‐Cluster Fracture Networks in Permeable Formations

AbstractComplex natural fracture networks typically consist of multiple clusters, whose connectivity is rarely quantified. Therefore, for each identified fracture network, we propose a connectivity metric that accounts for individual fracture clusters and their interactions. This metric evaluates contributions from all fracture clusters, considering their relative sizes and interactions among the isolated clusters, which in turn depend on the hydraulic conductance of the interconnecting rock matrix. Furthermore, we investigate how the system connectivity depends on fracture sealing, alterations of central clusters, and cluster linkage. Fracture sealing strongly impacts overall fracture connectivity, with 5 percent of sealed fractures reducing connectivity by 20 percent. The connectivity reduction is small when transitioning the central cluster from the largest to the smallest one. However, the largest cluster significantly contributes to overall connectivity, while the smallest one contributes minimally. Natural fracture networks increase connectivity by linking more clusters, with heterogeneity and anisotropy playing pivotal roles.

Development and implementation of a core genome multilocus sequence typing scheme for Yersinia enterocolitica: a tool for surveillance and outbreak detection.

Yersinia enterocolitica (Y. enterocolitica) is the most frequent etiological agent of yersiniosis and has been responsible for several national outbreaks in Norway and elsewhere. A standardized high-resolution method, such as core genome Multilocus Sequence Typing (cgMLST), is needed for pathogen traceability at the national and international levels. In this study, we developed and implemented a cgMLST scheme for Y. enterocolitica. We designed a cgMLST scheme in SeqSphere + using high-quality genomes from different Y. enterocolitica biotype sublineages. The scheme was validated if more than 95% of targets were found across all tested Y. enterocolitica: 563 Norwegian genomes collected between 2012 and 2022 and 327 genomes from public data sets. We applied the scheme to known outbreaks to establish a threshold for identifying major complex types (CTs) based on the number of allelic differences. The final cgMLST scheme included 2,582 genes with a median of 97.9% (interquartile range 97.6%-98.8%) targets found across all tested genomes. Analysis of outbreaks identified all outbreak strains using single linkage clustering at four allelic differences. This threshold identified 311 unique CTs in Norway, of which CT18, CT12, and CT5 were identified as the most frequently associated with outbreaks. The cgMLST scheme showed a very good performance in typing Y. enterocolitica using diverse data sources and was able to identify outbreak clusters. We recommend the implementation of this scheme nationally and internationally to facilitate Y. enterocolitica surveillance and improve outbreak response in national and cross-border outbreaks.

Unwrapping the Native Plant Black Box: Consumer Perceptions and Segments for Target Marketing Strategies

The increasing demand for sustainable products has helped spur demand for native plants. This study used an online survey of 2066 US consumers, a factor analysis, and Ward’s linkage cluster analysis to identify unique customer segments in the native plant marketplace. The following three clusters were identified: native averse (31.6%), native curious (35.7%), and native enthusiast (32.7%). The native enthusiast cluster agreed strongly with positive statements related to native plant perceptions and attributes. The native averse cluster exhibited the lowest level of agreement with these items and the greatest level of agreement with negative or neutral statements about native plants. The native curious cluster was intermediate between the other clusters but generally agreed with positive attributes. Demographic characteristics impacted cluster membership. The marketing implications are discussed.

Learning to cluster person via graph convolution networks for video‐based person re‐identification

SummaryUnsupervised person re‐identification based on video sequences can be applied to surveillance systems and is attracting much more attention. It aims to spot specific person in other scenes captured by different cameras. This work explores an innovative strategy, namely, learning to cluster unlabeled person in the videos through graph convolutional networks. In this article, we find that the possibility of inter‐frame linkage can be inferred from context. Therefore, a pose‐guided topology linkage clustering framework is proposed. Our framework consists of three modules: (i) a pose‐guided representation module; (ii) a pose‐guided embedding module; (iii) a link prediction module. First, the representation coding alone is performed at the level of relational induction bias, embedding the implicit pose structure information in image features. Then, based on the consideration of the topology relationship between adjacent and cross‐frame, graph convolutional network is introduced to infer the likelihood of linkage between frame nodes. Experiments show that the proposed method demonstrates excellent scalability in addition to being an effective response to person clustering in case of changes, and does not need the number of clusters as a prior.

An overview of Metal–Organic Frameworks as potential platform: Sensing for detection environmental pollutants and drug delivery applications

Metal Organic Frameworks (MOFs) represent a novel class of materials, characterized by their ability to form extended porous crystalline structures through the linkage of inorganic clusters or metallic ions with organic ligands. The availability of broad range of metal centres and type of organic linkers gives us opportunity for tailoring physical and chemical properties of MOFs. These modified MOFs opens up new channels for the customization of new devices needed for specific purposes. Though MOFs have been explored for significant applications in different sectors such as gas storage, proton conduction, biotechnological, biomedical sciences, catalysis, and separation, but drug delivery and the sensing applications has been taken exclusively at the manuscript. So, this article entails an overview of various synthetic methodology of low dimensional MOFs such as 1D and 2-D MOFs, and also focuses on their applications in sensing and drug delivery. This stems from their remarkable catalytic chemistry and their compatibility with a broad spectrum of materials. Furthermore, the 2-D layered structure of MOFs and their distinctive coordination of metal ions with organic ligands have emerging interest in the recent years.

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.

Regionalization of hydroclimate variables in the contiguous United States

We apply a hierarchical clustering algorithm to the Parameter-elevation Relationships on Independent Slopes Model (PRISM) database. The method employs linkage clustering while forcing spatial contiguity. We apply it to the lower-48 United States, deriving regions that are based on temperature and precipitation averages and anomalies, as well as statistical parameters underlying several drought and intense precipitation measures. Resulting regions make intuitive sense from the perspective of driving influences on temperature and precipitation averages and anomalies, and are compatible with results from another empirically derived clustering scheme. Regions selected for individual variables show high similarity across different time frames. There is slightly less similarity when comparing regions created for different monthly or daily hydroclimate variables, and relatively low similarity between monthly vs. daily measures. It is unlikely that any one regionalization solution could summarize hydroclimate extremes given the wide range of variables used to describe them, but geographically sensitive datasets like PRISM and flexible algorithms provide useful methods for regionalization that can aid in drought monitoring and forecasting, and with impacts and planning associated with heavy precipitation.

Morphological Diversity and Genetic Parameter of Okra (Abelmoschus esculentus L. Moench) Genotypes.

<b>Background and Objective:</b> Considering that the potential for okra as an anti-diabetic is very high, while okra productivity in Indonesia is still low, a plant breeding program through variety development is needed. One of the initial activities that needs to be carried out is the characterization of various genotypes, both quantitative and qualitative characters. This research aimed to obtain information on the diversity of morpho-agronomic characters in okra genotypes. <b>Materials and Methods:</b> The experiment was conducted as a randomized block design, one factor is genotype with three replications. The materials used in this research were 20 okra genotypes. The experimental units used in this research were 60 units. Each experimental unit consists of 10 sample plants. Analysis of quantitative character variations used PKBT-STAT 3.1. Cluster analysis was carried out with PBSTAT-CL 2.1.2 with the Gower dissimilarity and average linkage clustering methods. Furthermore, analysis was carried out using SAS OnDemand for Academics to see the distinguishing characteristics between clusters. <b>Results:</b> There were differences in okra genotypes based on qualitative and quantitative characteristics. The most diverse quantitative character is the yield component, which is the fruit character. Variance in genetic and heritability showed broad and high criteria, respectively. Based on cluster analysis results, okra genotypes were grouped into 3 clusters with a cophenetic distance value of 0.40. Cluster 1 consists of 9 genotypes. Cluster 2 consists of 10 genotypes. Cluster 3 consists of 1 genotype the Red Hill Country genotype. The grouping in cluster analysis was carried out based on leaf width, number of fruits, fruit weight, fruit diameter and carpel thickness character. <b>Conclusion:</b> This diversity of okra germplasm can facilitate plant breeding activities in the future by selecting genotypes to serve as parents according to the objectives carried out.

Micro-morphological study of three members of genus <i>Plectranthus</i> L. (Lamiaceae) in Nigeria

Micro-morphological studies of three members of the Genus Plectranthus L. (Lamiaceae) in Nigeria were conducted to investigate the anatomical characteristics of taxonomic importance. The following plant samples were collected from various locations in Southwestern Nigeria, namely Plectranthus aegyptiacus (Forssk.) C. Chr., Plectranthus esculentus N.E.Br. and Plectranthus monostachyus (P. Beauv.) B. J. Pollard. The anatomical study of the lamina epidermis, venation pattern and petiole were performed. The Single Linkage Cluster Analysis (SLCA) and one-way Analysis of Variance (ANOVA) with the Duncan Multiple Range Test (DMRT) were used to analyze the parameters, and photomicrographs were employed to document the observations. Foliar epidermal characteristics such as epidermal cell shape, anticlinal wall pattern, stomata types, trichome types and ergastic substances are essential in delimiting the species. The identification of numerous starch grains delimits P. monostachyus from the other species, a straight anticlinal wall pattern delimits P. esculentus, and the presence of abundant crystal sands delimits P. aegyptiacus. Quantitatively, stomata index (S.I.) and trichome length for both adaxial and abaxial surfaces were significantly different (P≤0.05) among the species. The venation pattern of the studied plant samples also revealed specific characteristics. Irregular vein spacing, eucamptodromous secondary vein category and polygonal areole shape delimit P. esculentus from the other species.

What Stops to Switch on to Solar Energy? An Empirical Evidence from Pakistan

This is an exploratory and qualitative study based on a survey. The study aims to generate a list of factors hindering switching to solar energy systems. The study design comprises the literature review, data analysis and discussion. The population under study includes stakeholders of solar energy systems, e.g., solar penal producers, importers, sellers, marketers, buyers/consumers, regulators, etc. Twenty-one samples are drawn from within the stakeholders, constituting a non-probability purposive sample of experts. The data are collected using a matrix-type questionnaire from experts. To perform analysis on data, the classical technique of Interpretive Structural Modeling (ISM) combined with Matriced' Impacts Cruise's Multiplication Appliquee a U.N. Classement (MICMAC) is used. Findings of ISM modeling show that limited production and uncertainty about after sale service fall at Level I, in contrast, lack of expertise and lack of social awareness fall at Level V & VI (respectively) of the model. All other barriers lye in middle on the continuum of bottom-to-top. It is worth mentioning that the barriers contained at Level I are the least important and those held at Level V & VI are the most important ones. Findings of MICMAC show that barriers, namely limited production and uncertainty about after sale service, fall in the independent cluster. In contrast, barriers, namely weak infrastructure structure, non-availability of feed-in tariff (fit) system, limited production and uncertainty about after sale service, fall in the dependent cluster. All the other barriers fall in the linkage cluster. This is an original valuable study based on real-time data collected from stakeholders that contributes new information about inter-relationships of barriers about the phenomenon under study. The study has various implications for the stakeholders, i.e. solar penal producers, wholesalers, retailers, importers, marketers, regulators, buyers, society and economy, etc.