Paper Info
Title
Incremental mining of closed frequent subtrees
Abstract
We study the problem of mining closed frequent subtrees from tree databases that are updated regularly over time. Closed frequent subtrees provide condensed and complete information for all frequent subtrees in the database. Although mining closed frequent subtrees is in general faster than mining all frequent subtrees, this is still a very time consuming process, and thus it is undesirable to mine from scratch when the change to the database is small. The set of previous mined closed subtrees should be reused as much as possible to compute new emerging subtrees. We propose, in this paper, a novel and efficient incremental mining algorithm for closed frequent labeled ordered trees. We adopt a divide-and-conquer strategy and apply different mining techniques in different parts of the mining process. The proposed algorithm requires no additional scan of the whole database while its memory usage is reasonable. Our experimental study on both synthetic and real-life datasets demonstrates the efficiency and scalability of our algorithm.
Year
DOI
Venue
2010
10.1007/978-3-642-16184-1_25
Discovery Science
Keywords
Field
DocType
different part,whole database,previous mined closed subtrees,different mining technique,mining process,closed frequent subtrees,efficient incremental mining algorithm,frequent subtrees,experimental study,proposed algorithm,divide and conquer
Data mining,Computer science,Artificial intelligence,Data mining algorithm,Complete information,Machine learning,Scalability
Conference
Volume
ISSN
ISBN
6332
0302-9743
3-642-16183-9
Citations 
PageRank 
References 
2
0.37
12
Authors
2
Name
Order
Citations
PageRank
Viet Anh Nguyen112719.08
Akihiro Yamamoto213526.84