A Fuzzy Threshold Based Modified Clustering Algorithm for Natural Data Exploration

Clustering is a group of unsupervised statistical techniques commonly used in many disciplines. Considering their applications to fish abundance data, many technical details need to be considered to ensure reasonable interpretation. However, the reliability and stability of the clustering methods have rarely been studied in the contexts of fisheries. This study presents an intensive evaluation of three common clustering methods, including hierarchical clustering (HC), K-means (KM), and expectation-maximization (EM) methods, based on fish community surveys in the coastal waters of Shandong, China. We evaluated the performances of these three methods considering different numbers of clusters, data size, and data transformation approaches, focusing on the consistency validation using the index of average proportion of non-overlap (APN). The results indicate that the three methods tend to be inconsistent in the optimal number of clusters. EM showed relatively better performances to avoid unbalanced classification, whereas HC and KM provided more stable clustering results. Data transformation including scaling, square-root, and log-transformation had substantial influences on the clustering results, especially for KM. Moreover, transformation also influenced clustering stability, wherein scaling tended to provide a stable solution at the same number of clusters. The APN values indicated improved stability with increasing data size, and the effect leveled off over 70 samples in general and most quickly in EM. We conclude that the best clustering method can be chosen depending on the aim of the study and the number of clusters. In general, KM is relatively robust in our tests. We also provide recommendations for future application of clustering analyses. This study is helpful to ensure the credibility of the application and interpretation of clustering methods.

PurposeThe K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.Design/methodology/approachThis study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.FindingsThe performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.Originality/valueThe KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

This research attempts to identify an alternative approach to measuring Gross National Happiness (GNH).It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH.It uses four widely used AI approaches to estimate GNH scores from online content such as social media posts and news websites to provide a cost-practical alternative approach to the traditional approach of GNH measurement.Traditional GNH measurement uses expensive and time-consuming surveys that take place infrequently, once every few years.This research addresses some of the limitations of conventional surveys.We use existing 'off the shelf ' NLP models to encode social media-based text data and then conduct sentiment analysis, testing several AI classification approaches to estimate GNH scores.The four classification approaches tested to estimate the sentiment scores are the Multi-layer Perceptron (MLP) classifier, decision tree, linear model, and K-nearest neighbors (K-NN).The results show the MLP approach to achieve the highest accuracy on the test data set.The research concludes that this approach can estimate a country's GNH scores more cost-effectively and timely than the traditional approach.Applying AI on social media and news websites provides a real-time understanding of society's progress, concerns, and challenges.This can help cost-effectively influence effective policy-making.A pilot study was conducted to measure the GNH of the Royal Kingdom of Bhutan.

Gross National Happiness (GNH), a concept that is rooted in Buddhist philosophies, underpins a range of educational policies in Bhutan. The Educating for GNH (EGNH) initiative was launched a decade ago to promote GNH values throughout the school system. However, whether EGNH has been a success is still contested with evidence lacking to support either view. Several issues arising from the implementation and evaluation of this initiative remain unaddressed. The five pathways to the EGNH have not been effectively institutionalized. Principals and teachers still lack the motivation and competency to infuse and integrate GNH values. More importantly, the Performance Management System (PMS) is questioned as a valid framework to monitor the primary purpose of providing adequate feedback and trustworthy recommendations for promoting and improving GNH practices. Based on the review of literature and some anecdotal evidence, the authors present the Theory of Practice Architectures as a new direction for investigating EGNH practices in the Bhutanese schools as it provides the theoretical framing for interrogating and potentially transforming educational practices.

In this study, clustering based method for activation detection in functional magnetic resonance imaging (fMRI) is employed. Moreover, some features are obtained by fitting two models namely FIR filter and Gamma function, to hemodynamic response function (HRF). After applying clustering methods (that require number of clusters as an input) to feature space, our simulations show that number of clusters can affect activation detection significantly. Therefore a newly proposed clustering algorithm namely evolving neural gas (ENG) that gives optimal number of clusters is exploited. In addition to ENG, the result of four clustering algorithms namely k-means, fuzzy C-means, neural gas, and clara in different number of clusters are evaluated. The results show that the best activation detection is taken place using obtained optimal number of clusters.

With the development of shale gas reservoirs, various fracture propagation models have been developed to predict hydraulic fracture geometry by considering the stress shadow effects. Also, many researchers have been working on development of various production-simulation approaches to simulate production from the complex fracture geometries. The objective of this study is to combine the fracture propagation modeling and reservoir simulation to optimize production in shale gas reservoirs. First, we have developed a semi-analytical model to simulate shale gas production from the complex non-planar fracture geometry with varying fracture width and fracture permeability along fracture length. The important gas transport mechanisms including gas slippage, gas diffusion, and gas desorption are considered. Second, we integrate the semi-analytical model with a three-dimensional fracture propagation model, which fully couples elastic deformation of the rock and fluid flow to simulate complex hydraulic fracture propagation, to optimize fracture treatment design in Marcellus shale gas reservoirs. Specifically, the fracture propagation model is utilized to predict the more-realistic non-planar fracture geometry with varying fracture width and fracture permeability along fracture length. In this study, the effect of varying number of perforation clusters within a given stage ranging from 2 to 5 on the fracture propagation was investigated. Three values of stage spacing were considered including 100 ft, 200 ft, and 300 ft. After predicting the non-planar fracture geometry, the semi-analytical model is employed to simulate production from such fracture geometries. According to the well productivity, the optimal number of clusters per stage for varying stage spacing is discussed and determined. In addition, we compared the well performance with the optimal number of perforation clusters for the stage spacing of 100 ft, 200 ft, and 300 ft under a given horizontal well length of 2,500 ft. Furthermore, we used the integrated approach to analyze a well performance from Marcellus shale gas reservoirs. Through the field case study, the more-realistic non-planar fracture geometry was quantified. Also, the difference between the non-planar fracture geometry and ideal planar fracture geometry with equal fracture width and fracture half-length was compared. Finally, the optimal fracture treatment design for the field well development was recommended. This work can provide new insights into optimization of number of perforation clusters per stage within a given fracture stage and a better understanding of the non-planar fracture geometry in shale gas reservoirs.

This paper presents a participatory methodological framework to identify Gross National Happiness (GNH) issues at the smallholder level in Bhutan. GNH is a development paradigm of Bhutan that has increasingly drawn international attention. Its four pillars are sustainable and equitable socioeconomic development, preservation of the environment, preservation and promotion of culture, and promotion of good governance. Since GNH is usually discussed at the national level, its domains and indicators have been defined through a top-down intellectual exercise, with possibly limited relevance of the major issues for most rural Bhutanese, which represent 69 percent of the country’s population. The methodology applied in this study was useful in identifying key GNH issues from a systems perspective at the smallholder level. Socioeconomic development and the environmental aspects were found to be the pertinent issues. The study also revealed trade-offs and dependencies among the four GNH pillars and their indicators. Inclusive policies are needed to address the concerns of smallholder farmers. If GNH is to work for the present and future generations, then it is essential to embrace the GNH issues of smallholder farmers who compose the backbone of the Bhutanese population. Further, the GNH concept is currently a mix of issues and indicators. Translating the issues identified by the study into indicators is required to properly evaluate the progress at the farm level and to support GNH policy development.

PurposeThe purpose of this paper is to elucidate the moral dimensions of accounting by examining the case of Gross National Happiness (GNH) in Bhutan, and to propose a new approach to accounting that is grounded in the Buddhist principle of the Middle Path. This approach aims to promote well-being and happiness, contrasting with traditional accounting practices.Design/methodology/approachThe paper outlines the core concepts of the Middle Path theory and GNH. The authors first problematise the role of traditional accounting in the well-being and happiness project. The authors explore accountability from the Middle Path perspective, which is a key aspect of Buddhist philosophy. Using the concept of Middle Path accountability and GNH in practice, the authors then examine accounting in terms of the four “immeasurable moral virtues” (tshad med bzhi) of the Middle Path. The authors conclude by highlighting the value of the Middle Path for conceptualising accountability and emancipating contemporary accounting from its ethical and theoretical constraints.FindingsThis paper compares the application of traditional accounting and accountability with the Middle Path and GNH practices. The authors find that ethical discourses in traditional accounting and accountability are not compatible with the values of the Middle Path, thereby limiting the scope of accounting and accountability. This constraint is overcome by introducing four “immeasurable moral virtues” (tshad med bzhi) of Buddhism, which promote spiritual development (wisdom) to replace the existing ethical strands of traditional accounting and accountability to support the well-being and happiness project.Research limitations/implicationsThe study is limited to the review of concepts in GNH and Buddhist philosophy. More empirical studies in different contextual settings could increase understanding of how the practice of Middle Path and GNH could drive the project of well-being and happiness through accounting.Practical implicationsThe paper seeks to contribute to the operationalisation of GNH in organisation by framing social and well-being accounting grounded in the Middle Path theory. The authors also seek to clarify the role of accounting as a social and moral practice.Social implicationsSituated within the fields of social and moral accounting, the paper seeks to elevate the potential role of accounting in the promotion of well-being and happiness of people and other sentient beings. By applying four moral virtues of love, compassion, appreciative joy and equanimity in accounting, the authors seek to enhance the role of accounting that could potentially reduce poverty, social inequity, corruption and promote harmony and cultural well-being.Originality/valueThis study undertakes a conceptual integration of the GNH and Middle Path philosophy to understand the theoretical and ethical implications of traditional accounting and accountability. This contribution to the literature expands the possibilities of accounting and accountability on social and well-being accounting by introducing the Middle Path and GNH concepts.

Green education as mentioned in Thakur S. Powdyel’s My Green School: An Outline (2014) was developed to support democratic Bhutan’s initiative ‘Educating for Gross National Happiness’ (EGNH), pinned on the nature-culture axis. The initiative motivated the educational institutions to imbue their teaching-learning environment with the values of Gross National Happiness (GNH). However, in terms of holistic development in modern education, the concept requires an integrated understanding beyond the explicit meaning of green, which primarily denotes nature and the environment. The eight dimensions of green school, denoted by the term ‘Sherig Mandala’ and designed in a concentric sense, hold a critical significance. Besides dealing with the idea of education through the natural environment, the concept also has a major claim on an individual’s life and learning on a moral level, integrating the physical and psychosocial ambience. It attempts to establish that the process of learning is the key to understanding the principles of life. With the core value of education being jeopardized in modern times, the paper attempts to ascertain the meaning and purpose of learning for a holistic life. The inherent meanings of the elements of greenery such as natural, social, cultural, intellectual, academic, aesthetic, spiritual and moral were examined to comprehend their philosophical intent. The significance of the concept was examined not in isolation but in correlation with the nine domains of Bhutan’s developmental philosophy known as GNH. Further, the paper has also affirmed that the conceptualization of ‘Green School’ in Bhutan is quite different from other countries around the world because it focuses not on the buildings and infrastructure, but on the behaviour and the core inner aspects that each child requires. This is to emphasize the fact that only the nobility of mind, heart and hands would empower the learners.

We use daily happiness scores (Gross National Happiness (GNH)) to illustrate how happiness changed throughout 2020 in ten countries across Europe and the Southern hemisphere. More frequently and regularly available than survey data, the GNH reveals how happiness sharply declined at the onset of the pandemic and lockdown, quickly recovered, and then trended downward throughout much of the year in Europe. GNH is derived by applying sentiment and emotion analysis–based on Natural Language Processing using machine learning algorithms–to Twitter posts (tweets). Using a similar approach, we generate another 11 variables: eight emotions and three new context-specific variables, in particular: trust in national institutions, sadness in relation to loneliness, and fear concerning the economy. Given the novelty of the dataset, we use multiple methods to assess validity. We also assess the correlates of GNH. The results indicate that GNH is negatively correlated with new COVID-19 cases, containment policies, and disgust and positively correlated with staying at home, surprise, and generalised trust. Altogether the analyses indicate tools based on Big Data, such as the GNH, offer relevant data that often fill information gaps and can valuably supplement traditional tools. In this case, the GNH results suggest that both the severity of the pandemic and containment policies negatively correlated with happiness.

Bhutan has become well known internationally for its policy of Gross National Happiness (GNH), an alternative approach to economics and governance which directs Bhutan’s development toward environmental sustainability and societal well-being. In addition to providing an overview of GNH, the aim of this chapter is to consider whether GNH should be viewed as a Buddhist perspective on the economy and development. I argue that in order to make sense of the way proponents of GNH understand and represent GNH one needs to make reference to central Buddhist values and principles. Further, I suggest that we can discern two forms of Buddhist modernism in the phenomena of GNH: one nationalist, which closely associates GNH with the Bhutanese monarchy and the sovereignty of Bhutan, and another that connects GNH with a universalist and secularized form of Buddhism. While the former aspect of GNH is more associated with Bhutanese citizens, and the latter is oriented to a transnational audience, I argue that the boundary between these two forms of Buddhist modernism as evident in GNH cannot be drawn on the basis of citizenship but rather reflects two different functions of GNH that co-exist in tension.

BackgroundCluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing “outliers” among the objects being clustered. Few clustering algorithms currently deal directly with the outlier problem. Furthermore, existing methods for identifying the number of clusters still have some drawbacks. Thus, there is a need for a better algorithm to tackle both challenges.ResultsWe present a new approach, implemented in an R package called Thresher, to cluster objects in general datasets. Thresher combines ideas from principal component analysis, outlier filtering, and von Mises-Fisher mixture models in order to select the optimal number of clusters. We performed a large Monte Carlo simulation study to compare Thresher with other methods for detecting outliers and determining the number of clusters. We found that Thresher had good sensitivity and specificity for detecting and removing outliers. We also found that Thresher is the best method for estimating the optimal number of clusters when the number of objects being clustered is smaller than the number of variables used for clustering. Finally, we applied Thresher and eleven other methods to 25 sets of breast cancer data downloaded from the Gene Expression Omnibus; only Thresher consistently estimated the number of clusters to lie in the range of 4–7 that is consistent with the literature.ConclusionsThresher is effective at automatically detecting and removing outliers. By thus cleaning the data, it produces better estimates of the optimal number of clusters when there are more variables than objects. When we applied Thresher to a variety of breast cancer datasets, it produced estimates that were both self-consistent and consistent with the literature. We expect Thresher to be useful for studying a wide variety of biological datasets.

To find an optimal cluster size and number of clusters for a reasonable estimate of the prevalence of cataract blindness in people aged > or = 50 years in 19 rural districts of a state in India. Cluster sampling methodology was used in 19 rural districts of Karnataka State, India. In each district, 15 clusters were randomly selected and 90 people aged > or = 50 years were examined in each cluster. As a result the visual acuity and lens status of a total of 22,218 people were assessed. For each district, the design effect for cluster size ranging from 20 to 90 was calculated and the optimal cluster size and the required number of clusters to achieve an accuracy of 1% errors and 80% confidence was assessed. The age and gender adjusted prevalence of cataract blindness varied from 1.58% to 7.24%, which justifies district level surveys. The design effect is nearly 1.5 for clusters of sizes 30 and 40. With an average prevalence of 4.93% with 1% error and 80% confidence level, the optimal number of clusters is 37 and 28 for a cluster size of 30 and 40 respectively and the average sample size for a district around 1100. Rapid assessments for cataract blindness in those aged > or = 50 years can be conducted at district level in India with existing resources and at affordable costs. These provide reliable data, essential for effective monitoring and planning. Other parameters, for instance, surgical coverage can also be assessed. The availability of standardized software for data entry and analysis and strict adherence to survey procedures is essential.

This paper explores how a growing trend towards neoliberalization throughout Bhutan manifests within environmental governance in particular. Bhutan’s well-known Gross National Happiness (GNH) development strategy can be seen to represent a shift towards a variegated governmentality more generally that increasingly exhibits neoliberal tendencies as the country seeks to negotiate its further integration into the global economy. Part of this integration entails efforts to promote ecotourism as a key element of the country’s future conservation strategy. Ecotourism has been described as a growing manifestation of a “neoliberal environmentality” (Fletcher, 2010) within environmental conservation policy and practice, and hence Bhutan’s promotion of ecotourism can be seen as contributing to the promotion of neoliberal conservation. Yet in practice, my analysis demonstrates that environmental governance in Bhutan is a complex of external neoliberal influences filtered through local formal and informal institutions, specifically a Buddhist worldview, a history of state paternalism and the Gross National Happiness governance model, all of which express contrasting governance rationalities. This study thus contributes to governmentality studies by promoting a variegated environmentality perspective that calls for more nuanced analyses beyond “variegated” neoliberalization. This perspective also affords a holistic understanding of discrepancies between the vision and execution of neoliberal conservation that can be attributed to the articulation of alternative rationalities in policy formulation and implementation.

Gross National Happiness, by definition, is closely linked to mental health, which is a state of well-being in which the individual realises his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to contribute to his or her community. Across cultures, concepts of mental health, as with GNH, include subjective well-being; perceived self-efficiency; autonomy; competence; inter-generational independence; and self-actualisation of one's intellectual and emotional potential, among others. It is generally agreed that the concept of mental health is broader than a lack of mental disorders. Mental health must address the entire spectrum of issues affecting the mental well-being of all sectors of society.