answer follow questions 4

Introduction to Dataming

Refer text book: Introduction-to-Data-Mining-2nd-Edition-by-Pang-Ning-Tan

Exam Questions:

Question1:

Most frequent pattern mining algorithms consider only distinct items in a transaction. However, multiple occurences 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 itemsets efficienly considering multiple occurences of items using the Apriori algorithmn?

Question2:

Why is the outliner mining important? Briefy describe the different approaches behind statistical-based outlier detection and distance-based outlier detection.

Question3: Describe why concept hierachies are useful in datamining?

Question4: List and Describe three of the important chararcteristics of decision tree induction algorithms?

Question5: In real-world data, tuples with missing values for some attributes are a common occurence. Describe the strategies for handling this problem