Article
EVALUATING THE EFFECTIVENESS OF PROCEDURE INFORMATION USING DATA MINING ALGORITHMS
To illustrate the procedures, we use a sample of U.S. students' answers to problem-solving questions on the 2022 PISA (N = 426). Classifier development operations are carried out utilizing the shown methods after the creation and selection of concrete features. All of the methods yielded good results in terms of categorization accuracy. Recommendations for choosing classifiers are provided, taking into account research topics, classifier interpretability, and classifier simplicity. Both supervised and unsupervised learning approaches provide outcomes that are explained.With the advent of big data, it has become more difficult to glean insights from large datasets. This study aims to provide light on the possible benefits, drawbacks, and ramifications of using advanced analytical techniques to extract significant patterns, correlations, and trends from large datasets by examining the overlap between data mining and big data.The fundamental goals of this research are to determine the most effective data mining methods for large-scale data analysis, evaluate their scalability and computing efficiency, and get a knowledge of their potential to uncover previously unknown insights. In order to better understand the interplay between data mining approaches and the complexities of big data, this study will analyze the relevant literature and perform experiments. With the advent of the age of big data, there has been an unprecedented flood of data in many fields, requiring novel methods to be developed in order to glean useful insights. Knowledge discovery relies heavily on data mining, which has developed to meet the problems provided by big data's volume, velocity, and diversity.
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