About: BookChapter: Incremental Mining of Closed Frequent Subtrees

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About: BookChapter: Incremental Mining of Closed Frequent Subtrees

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type	http://scigraph.springernature.com/ontologies/core/BookChapter
label	BookChapter: Incremental Mining of Closed Frequent Subtrees
owl:sameAs	http://lod.springer.com/data/bookchapter/978-3-642-16184-1_25
Property: Scigraph ID	73721927a99cf4b69eaa2c044d1a672e
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Property: DOI	10.1007/978-3-642-16184-1_25
Property: web page	https://link.springer.com/10.1007/978-3-642-16184-1_25
Property: copyright year	2010-01-01 00:00:00(xsd:date)
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http://scigraph.sp...logies/core/title	Incremental Mining of Closed Frequent Subtrees
Property: copyright holder	Springer-Verlag Berlin Heidelberg
Property: language	En
http://scigraph.sp...ies/core/pageLast	370
Property: has book	http://scigraph.springernature.com/things/books/7353d98bb527ce3bcc62cbfbfe31f22e
http://scigraph.sp...es/core/pageFirst	356
Property: abstract	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.
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