1) Most frequent pattern mining algorithms consider only distinct items in a transaction. However, multiple occurrences of an item in the same shopping basket, such as four cakes and three jugs of milk, can be important in transactional data analysis. How can one mine frequent item sets efficiently considering multiple occurrences of items using the Apriori algorithm?
2) Why is outlier mining important? Briefly describe the different approaches behind statistical-based outlier detection and distance-based outlier detection.
3) describe why concept hierarchies are useful in data mining.
4) List and describe three of the important Characteristics of decision tree induction algorithms.
5) In real-world data, tuples with missing values for some attributes are a common occurrence. Describe the strategies for handling this problem.
