Association Pattern Mining: Advanced Concepts
- Charu C. AggarwalAffiliated withIBM T.J. Watson Research Center Email author
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.
- Association Pattern Mining: Advanced Concepts
- Book Title
- Data Mining
- Book Subtitle
- The Textbook
- pp 135-152
- Print ISBN
- Online ISBN
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- Additional Links
- Industry Sectors
- eBook Packages
- Author Affiliations
- 2. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
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