Cite the following discussion points:

After reading these articles, what would one gain?

After reading these articles, one would understand what data mining is, its various sub-branches, and the challenges and unanswered questions in the field. One would understand the importance of privacy and security in data mining, the problem of the "curse of dimensionality," and the need for organizational transparency and interpretability in data mining algorithms. They will also learn about the various methods used in data mining and their applications.

What questions remain unanswered?

-How can we ensure privacy and security in data mining? Data mining involves dealing with sensitive data and ensuring privacy and security is a big concern. Many questions remain about effectively anonymizing data, protecting against data breaches, and ensuring data mining practices comply with privacy laws. -How can we handle the "curse of dimensionality" in data mining? The "curse of dimensionality" refers to the problem that high-dimensional data can be sparse, making it difficult to find patterns. Many questions remain about effectively reducing dimensionality and dealing with high-dimensional data. -How can we make data mining more transparent and interpretable? Many data mining algorithms, especially those based on machine learning, are often seen as "black boxes" because it is unclear how they generate their results. There are still many questions about how to make these algorithms more transparent and interpretable. -How can we deal with the issue of data quality in data mining? There are still many questions about ensuring data quality, dealing with missing or noisy data, and validating data mining results.

What did one gain from reading these articles? One has gained a comprehensive understanding of the current challenges in data mining, which could be helpful in one's field (Medical/Logistics) or those interested in the ethical and practical implications of data mining.

References: Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.