test 5.docx ITS-632 WK 10 University of the Cumberlands Data Mining (ITS-632-M22)-Full

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test 5.docx ITS-632 WK 10 University of the Cumberlands Data Mining (ITS-632-M22)-Full Term 1.What is the definition of data mining that the author mentions? How is this different from our curre... nt understanding of data mining? The application of data analysis techniques with the goal of extracting hidden knowledge from data by performing pattern recognition and predictive modeling activities is referred to as data mining. The application of data mining techniques to educational data from a Croatian higher education institution is described in this paper. Event logs from a real e-e-learning course's environment were used to conduct the analysis. Cluster analysis and decision trees were used as data mining approaches in this study. Cluster analysis was carried out by categorizing patterns into groups based on similarities in student behavior when using course materials. The method of interest for developing a representation of decision-making that permitted identifying classes of items for further investigation of how pupils learned was the decision tree (Tan et. al, 2018). Data mining is a widely used technique for analyzing big data sets and extracting necessary or valuable information. The purpose of a data mining program is to find hidden data patterns and correlations between parameters in large amounts of data. Educational data mining is the use of data mining tools to explore data in education. Various educational data is recorded in massive databases (Otey et. al., 2006). Th. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .. .. . . . . . . . . . . . . . . . . . .. . . . . [Show More]

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