Fuzzy Cluster Analysis: Application to Determining Metallicities for Very Metal-poor Stars

This work presents a first attempt to apply fuzzy cluster analysis (FCA) to analyzing stellar spectra. FCA is adopted to categorize line indices measured from LAMOST low-resolution spectra, and automatically remove the least metallicity-sensitive indices. The FCA-processed indices are then transferred to the artificial neural network (ANN) to derive metallicities for 147 very metal-poor (VMP) stars that have been analyzed by high-resolution spectroscopy. The FCA-ANN method could derive robust metallicities for VMP stars, with a precision of ∼0.2 dex compared with high-resolution analysis. The recommended FCA threshold value λ for this test is between 0.9965 and 0.9975. After reducing the dimension of the line indices through FCA, the derived metallicities are still robust, with no loss of accuracy, and the FCA-ANN method performs stably for different spectral quality from [Fe/H] ∼ −1.8 down to −3.5. Compared with traditional classification methods, FCA considers ambiguity in groupings and noncontinuity of data, and is thus more suitable for observational data analysis. Though this early test uses FCA to analyze low-resolution spectra, and feeds the input to the ANN method to derive metallicities, FCA should be able to, in the large data era, also analyze slitless spectroscopy and multiband photometry, and prepare the input for methods not limited to ANN, in the field of stellar physics for other studies, e.g., stellar classification, identification of peculiar objects. The literature-collected high-resolution sample can help improve pipelines to derive stellar metallicities, and systematic offsets in metallicities for VMP stars for three published LAMOST catalogs have been discussed.

Application of Fuzzy Cluster Analysis and Decision Making Algorithms for Choosing the Optimal Water Distribution Network Design

Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to facilitate multi-objective optimization in Water Distribution Network(s) (WDN) framework for a benchmark problem of Hanoi Network and a real-world problem, Pamapur Network, Telangana, India. Maximization of resilience, minimization of cost and minimization of leakages are considered in a multiobjective context which result in generation of Non-dominated WDN Strategies (NWDNS). In order to simplify the decision making process of engineers, Fuzzy Cluster Analysis (FCA) is employed to categorize NWDNS into groups. Thereafter, Dunn’s Cluster Validity Index (DCVI) is used for identification of optimal cluster size. Representative NWDNS i.e. RNWDNS for each sub-cluster is based on the maximum membership of NWDNS in the respective sub-cluster. Ranking of RNWDNS is performed with three decision making algorithms, namely, Preference Ranking Organization METHod for Enrichment of Evaluations-2 (PROMETHEE-2), Multicriterion Q-analysis-2 (MCQA-2) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Additive ranking rule is also applied to facilitate obtained ranks in group decision making environment to arrive at the optimal WDN. It is observed that 1020 NWDNS generated for Hanoi network are optimally classified into 18 clusters based on DCVI, and A13 representing RNWDNS 37 is found preferable. Whereas 272 NWDNS generated for Pamapur network are classified into 9 clusters where S6 is preferred (representing RNWDNS 203).

Risk assessment of OFDI for technology-based enterprises under the background of “One Belt One Road”—based on fuzzy cluster analysis

With the proposal of “One Belt One Road” initiative, more and more technology-driven enterprises in China are going abroad for foreign direct investment (FDI). Thus, how to correctly assess and reasonably prevent OFDI risk have become urgent tasks for enterprises. This paper takes 20 countries along the “Belt and Road” as the research object and combines the characteristics of technology-oriented enterprises to determine the risk assessment indicators at all levels from four aspects: politics and policy, economy and finance, society and culture, and technological risks. Using fuzzy cluster analysis, the countries along the “Belt and Road” are classified into four groups: low, medium-low, medium-high, and high risks, and corresponding countermeasures are proposed: technology-oriented enterprises should raise their political risk awareness, comprehensively assess the economic and financial environment of host countries before making FDIs, implement “localized” operation and management, and pay attention to the protection of intellectual property rights.

Fuzzy Cluster Analysis on Influencing Factors of College Student Scores

In the age of the Internet, the learning environment is increasingly diversified. It is of great importance to explore the factors that truly affect the college student scores. Focusing on 13 factors that potentially influence college student scores, this paper carries out a questionnaire survey on students of different grades from different colleges, conducts fuzzy processing of the collected data, randomly selects the processed data for initialization of attribute values. Then, the initialized data were subject to principal component analysis (PCA), fuzzy cluster analysis (FCA), and analysis of variance (ANOVA). Through the analysis, six factors were identified as the key factors affecting college student scores: family factor, Exam factor, exchange factor, learner factor, classmate factor, and campus factor. On this basis, the authors called for the concerted efforts from the school, teachers, and students for improving the teaching quality in colleges.