Mining System

1. a) List and describe the five primitives for specifying a data mining task.

b) Describe why concept hierarchies are useful in data mining.

c) Outliers are often discarded as noise. However, one person’s garbage could be another’s treasure. For example, exceptions in credit card transactions can help us detect the fraudulent use of credit cards. Taking fraudulence detection as an example, propose two methods that can be used to detect outliers and discuss which one is more reliable.

2. Recent applications pay special attention to spatiotemporal data streams. A spatiotemporal data stream contains spatial information that changes over time, and is in the form of stream data, i.e., the data flow in-and-out like possibly infinite streams.

(a) Present three application examples of spatiotemporal data streams.

(b) Discuss what kind of interesting knowledge can be mined from such data streams, with limited time and resources.

(c) Identify and discuss the major challenges in spatiotemporal data mining.

(d) Using one application example, sketch a method to mine one kind of knowledge from such stream data efficiently.

3a) Describe the differences between the following approaches for the integration of a data mining system with a database or data warehouse system: no coupling, loose coupling, semi-tight coupling, and tight coupling. State which approach you think is the most popular, and why.