Application Of Fuzzy Clustering Analysis Research Articles

Predicting Financial Failure of Public Teaching Hospitals: An Application of Fuzzy Clustering Analysis

Purpose –To develop a novel prediction model and determine the variables that best predict hospitals’ financial failures. Design/methodology/approach –A fuzzy C-means clustering analysis was conducted using financial ratios from MoH Hospitals’ financial databases. Hospital Fuzzy Financial Health Scores (HF-HFS) then were calculated the as the degree of financial failure measured through the selected n-cluster model. Findings –The results show that the number of teaching hospitals experiencing financial failure has increased. HF-FHS scores also were compared with the modified Altman Z scoresa commonly used financial distress measure in the hospital service sector. The HF-FHS scores in good and poor financial conditions differ significantly. The HF-FHS scores were also strongly correlated with the Altman Z scores. Discussion –The rising trend in the number of hospitals experiencing financial difficulties over the years may cause some hospitals in financial distress to fail to fulfill their obligations, which may disrupt the services provided. In such a situation, good management skills, which are the most important factor reducing the financial risks of hospitals, will not work after a certain point.Conclusions –The findings indicate Fuzzy C Means clustering as a viable option to evaluate the financial failure of hospitals compared to more traditional methods such as Altman Z scores. Hospitals still face financial pressures due to market and structural factors such as the global budget repayment system, pricing and collection time, and insufficient competition conditions.Predicting hospitals’ financial failures can help develop managerial policies and strategies to recognize and combat risks, improveperformance, and improve the current situation of hospitals.

Application of fuzzy clustering analysis in Classification of dominant channel of steam flooding: A case study of S121 Layer, Member 1 of Shawan Formation, Block P612, CF Oilfield

In order to determine the dominant channel of steam flooding in heavy oil reservoir P612 block of CF oilfield, the geological evaluation parameters of five dominant channels of steam flooding, namely permeability, reservoir thickness, sedimentary microfacies, coefficient of variation and interlayer frequency, were determined by combining reservoir static description with development dynamic analysis. According to the development dynamic characteristics, the sand body of the dominant channel was determined and the sample data was extracted. The fuzzy clustering analysis method was used to divide the two categories of the dominant channel and determine the interval value of its parameters. Thus, the geological classification evaluation standard of the dominant channel was established.

Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment

General education is an important part of university education aiming at cultivating students’ comprehensive quality and sense of responsibility. As professional sports colleges and universities, higher sports colleges implement the curriculum reform of general education relatively late, and there is a problem of curriculum system construction in the process of implementing general education. The purpose of this paper is to research and discuss the application of general education management in higher education based on fuzzy cluster analysis in the Internet environment. This paper first discusses the teaching mode in the Internet environment and then analyzes the application of fuzzy cluster analysis in general education. Through fuzzy cluster analysis, the students’ understanding of general education before and after learning general education courses is compared, which paves the way for the promotion of general education in colleges and universities. Finally, this paper takes the general education curriculum system of higher physical education colleges as the research object and conducts fuzzy cluster analysis on it. The experimental results show that before studying general courses, 66.7% of the students hope to improve their language communication and expression skills, and 52.8% and 46% of the students hope to improve their practical ability and logical thinking ability. After receiving general education, only 23.5% of students have improved logical thinking ability, and only 13.7% of students have improved language communication and expression skills. The results show that general education does not meet the expectations of students, general education in higher physical education colleges is not effective, and the realization of general education needs to be further improved. To this end, schools can purchase network information resources, cooperate with well-known universities at home and abroad, develop general education courses with a global perspective, train students’ spirit of independent thinking, and master correct thinking methods.

The Application of Fuzzy Clustering Analysis in Ore Classification

Based on the fuzzy clustering analysis, this article has obtained the classification of the known ore samples, obtained the dynamic cluster chart, and has given the samples best classification using variance analysis's principle.

A Study on the Application of Fuzzy Clustering Analysis in Juvenile Delinquency Prevention

A Study on the Application of Fuzzy Clustering Analysis in Juvenile Delinquency Prevention

Measuring the convergence of national accounting standards with international financial reporting standards: The application of fuzzy clustering analysis

A single set of accounting standards is considered the path to achieving accounting convergence globally. Given the important role that formal harmonization/convergence plays in the accounting profession and global capital markets, this study focuses on the methods and methodology for the measurement of formal accounting convergence. Based on our review and evaluation of the existing methods for measuring the level of harmonization/convergence between any two sets of accounting standards, we propose using a new method of matching and fuzzy clustering analysis to assess the convergence progress of national accounting standards (NAS) with International Financial Reporting Standards (IFRS) from whole and single standards, respectively. Single standards are clustered according to their convergence level, which may indicate further convergence emphasis. As an illustrative example, the achievements made in China are evaluated using this new method. The results reveal that this new method can measure the convergence level of NAS with IFRS more clearly and informatively.

Measuring the Convergence of National Accounting Standards with International Financial Reporting Standards: The Application of Fuzzy Clustering Analysis

The single set of accounting standards is conceived to be the path to bring accounting convergence globally. Given the important role of formal harmonization/convergence plays in accounting profession and global capital market, this study focuses on the methods and methodology for the measurement of formal accounting convergence. Based on reviewing and evaluating the existing methods for measuring the level of harmonization/convergence between any two sets of accounting standards, we propose a new method of matching and fuzzy clustering analysis to assess the convergence progress of national accounting standards (NAS) with International Financial Reporting Standards (IFRS) from whole and single standards respectively. Single standards are clustered according to their convergence level, which may indicate further convergence emphasis. As an illustrative example, the achievements made in China are evaluated by using this new method. The result reveals that this new method can measure the convergence level of NAS with IFRS more clearly and informatively.

Application of fuzzy clustering analysis on brain functional magnetic resonance image processing

模糊聚类分析作为模式识别中的一种重要的分类器,近年来在脑功能磁共振图像的处理与分析领域引起了极大的关注.在脑功能磁共振图像的处理与分析中应用模糊聚类技术需要解决的一个关键问题就是找出适合于脑功能磁共振图像处理的参数,如模糊指数、距离参数和聚类数等.如何寻找合适的参数,从而使得利用模糊聚类技术处理和分析脑功能磁共振图像得到最佳的聚类结果,成了目前模糊聚类技术研究的焦点问题.近年来模糊聚类分析在脑功能磁共振图像处理的应用中如何选择合适的参数问题进行了综述和讨论。

Fuzzy cluster analysis of high-field functional MRI data

Functional magnetic resonance imaging (fMRI) based on blood–oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver–operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true “neural” from false “vascular” activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure

Fuzzy clustering generates cluster membership weights that indicate how tightly each object is linked to its cluster relative to other clusters of a dendrogram. In a fuzzy clustering of the crustacean-zooplankton taxa of Lake Simcoe, a large (720 km2) hardwater lake in Ontario, Canada, we show how the membership weights can be used to rank all taxa for their contribution to the sampling unit (SU) classification, where the total number of SUs was 84 (7 years × 12 sampling sites). The validity of the results was confirmed by comparison with other more traditional methods of identifying variables important for object classifications and by permutation tests of matrix correlation before and after removal of low-ranked and highly ranked species. Fuzzy clustering of Lake Simcoe SUs also revealed (i) the likelihood of trends in zooplankton community composition over the 7-year period and (ii) differences in composition possibly related to sampling-station depth. In particular, the shallowest sampling station in southern Cook's Bay had a zooplankton community structure that differed significantly from other stations during all years of the study. As a preliminary screening or data exploration tool, fuzzy clustering is particularly useful for analysis of ecological data.

Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure

Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure

The application of fuzzy clustering analysis in process equipment importance classification

On the basis of the analytical hierarchy method and fuzzy sets theory, the analytical hierarchy model of the process equipment importance is established. The weighted values of the influence factors in subhierarchy and target hierarchy are obtained separately. According to the generalized Haimin distance, good classification results are found by fuzzy clustering analysis, and a fuzzy mathematical model for working out the inspection and maintenance plan of process equipments is proposed.

APPLICATIONS OF FUZZY MATHEMATICS IN EARTHQUAKE PREDICTION

This paper presents a direct method for fuzzy recognition of earthquake precursors and a method of fuzzy clustering analysis of temporal seismicity patterns for earthquake prediction. The direct method for fuzzy recognition of earthquake precursors is based on their fuzzy description by use of membership functions. If we use a suitable membership function, the abnormal features of a precursor can be more clearly recognized. The membership function proposed by Feng et al.. in 1981 is applied to reported changes in VP/VS, resistivity, and radon content. The basic idea of application of fuzzy clustering analysis to earthquake prediction is that time intervals with different sizes of the largest earthquake can be classified on the basis of fuzzy similar relations according to the various kinds of statistical indices of seismicity. The method based on the fuzzy equivalent relation includes the following steps: obtaining a fuzzy resemblance relation between samples, transforming it into a fuzzy equivalent relation, and classifying the original samples. The statistical indices of seismicity used by Feng et al. in 1984 is employed for clustering analysis of seismicity in the North-South Seismic Belt of China.