1. Data mining is related to machine learning and artificial intelligence, focusing on finding hidden patterns, relationships, and structures in large datasets. In CRISP and SEMMA processes, what are they used for in the mining of data?
2. What are the differences between supervised data mining models and unsupervised data mining models? Which are the easiest to calculate?
3. A measure of dissimilarity commonly used for quantitative variables is distance. The distance can be defined in many ways, leading to different results, known as what? If you are computing similarity measures, the dataset's variables must be on the same scale. One can scale or normalize the data via z-scores or min-max normalization. What can be used to identify these processes?