Homework 1 - Chapters 1 and 2

Questions are (mostly) from the book:

1. 1.4: Present an example where data mining is crucial to the success of a business. What data mining functionalities does this business need (e.g. think of the kinds of patterns that could be mined)? Can such patterns be generated alternatively by data query processing or simple statistical analysis?
2. 1.6: Based on your observations, describe another possible kind of knowledge that needs to be discovered via data mining methods but has not been listed in this chapter. Does it require a mining methodology that is quite different from those outlined in this chapter?
3. 1.9: What are the major challenges of mining a huge amount of data (e.g. billions of tuples) in comparison with mining a small amount of data (e.g. a data set of a few hundred tuples)?
4. 1.10: Outline the major research challenges of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data, or bioinformatics.
5. 2.5: Briefly outline how to compute the dissimilarity between objects described by the following:
   1. Nominal attributes
   2. Asymmetric binary attributes
   3. Numeric attributes
   4. Term-frequency vectors
6. 2.6: Given two objects represented by the tuples (22, 1, 42, 10) and (20, 0, 36, 8), compute the distance between the two objects using:
   1. Euclidean distance
   2. Manhattan distance
   3. Minkowski distance using q=3
   4. supremum distance
7. Ch 2: Give an example of a data set that can not be visualized with a 2D or 3D scatter plot. What method from this chapter would you use to visualize this data set?