Summarization: The output of association pattern mining is typically very large. For an end-user, a smaller set of discovered itemsets is much easier to understand and assimilate. This chapter will introduce a number of summarization methods such as finding maximal itemsets, closed itemsets, or nonredundant rules.
Querying: When a large number of itemsets are available, the users may wish to query them for smaller summaries. This chapter will discuss a number of specialized summarization methods that are query friendly. The idea is to use a two-phase approach in which the data is preprocessed to create a summary. This summary is then queried.
Constraint incorporation: In many real scenarios, one may wish to incorporate application-specific constraints into the itemset generation process. Although a constraint-based algorithm may not always provide online responses, it does allow for the use of much lower support-levels for mining, than a two-phase “preprocess-once query-many” approach.