Please use this identifier to cite or link to this item: https://doi.org/10.4018/978-1-60566-404-0.ch014
Title: Maintenance of frequent patterns: A survey
Authors: Feng, M.
Li, J.
Dong, G.
Wong, L. 
Issue Date: 2009
Citation: Feng, M.,Li, J.,Dong, G.,Wong, L. (2009). Maintenance of frequent patterns: A survey. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction : 273-293. ScholarBank@NUS Repository. https://doi.org/10.4018/978-1-60566-404-0.ch014
Abstract: This chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern maintenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and concise-representation-based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pattern maintenance are also discussed1. © 2009, IGI Global.
Source Title: Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction
URI: http://scholarbank.nus.edu.sg/handle/10635/78463
ISBN: 9781605664040
DOI: 10.4018/978-1-60566-404-0.ch014
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