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Incrementally Mining Frequent Patterns from Large Database

  • Yue-Shi Lee
  • Show-Jane YenEmail author
Chapter
Part of the Studies in Big Data book series (SBD, volume 8)

Abstract

Mining frequent patterns is an important task in data mining area, which is to find the itemsets frequently purchased together from a transaction database. However, the transactions will grow rapidly, such that the size of the transaction database becomes bigger and bigger due to the addition of the new transactions. The users may eager for getting the latest frequent patterns from the large database as soon as possible in order to make the best decision. Therefore, it has become an important issue to propose an efficient method for finding the latest frequent patterns when the transactions keep being added into the database. Although tree-based approaches have been recently adopted in most of the studies in this field, they have to re-scan the original database and generate a large tree structure. In this paper, we propose two efficient algorithms which only keep frequent items in a condensed tree structure. When a set of new transactions is added into the database, our algorithms can efficiently update the tree structure without scanning the original database.

Keywords

Data mining Frequent pattern Incremental mining Tree structure Transaction database 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringMing Chuan UniversityTaoyuanTaiwan

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