conclusions this study revealed the main components of knowledge storage. an enhanced preservation of explicit knowledge is recommended in the nursing educational system so that in the future, students and educators can easily access the same knowledge from storage sources and not from individuals who might be carrying only a single experience of the subject. results data analysis showed that n...

the present study examined the relationship between knowledge management and human resource development from the viewpoint of urmia university staff using descriptive survey study, has been done. 223 employees were randomly selected. data were collected and analyzed based on the research hypotheses, using pearson correlation coefficient, manova analysis, anova analysis, multiple regression anal...

Big data make it possible to mine learning information for insights regarding student performance in foreign language learning (FLL). Group-based learning is a usual method to improve FLL, whose effectiveness is greatly influenced by student groups. The general grouping method is to divide students into groups by their teacher manually, which is not timely or accurate. To overcome the shortcomi...

Sequential pattern discovery is a very important research topic in data mining and knowledge discovery and has been widely applied in business analysis. Previous works were focused on mining sequential patterns at a single concept level based on definite and accurate concept which may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from the rules dis...

In this paper, we discuss Conceptual Knowledge Discovery in Databases (CKDD) in its connection with Data Analysis. Our approach is based on Formal Concept Analysis, a mathematical theory which has been developed and proven useful during the last 20 years. Formal Concept Analysis has led to a theory of conceptual information systems which has been applied by using the management system TOSCANA i...

To microarray expression data analysis, it is well accepted that biological knowledge-guided clustering techniques show more advantages than pure mathematical techniques. In this paper, Gene Ontology is introduced to guide the clustering process, and thus a new algorithm capturing both expression pattern similarities and biological function similarities is developed. Our algorithm was validated...