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Frequent pattern mining without cand...

Cheung, William.

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Frequent pattern mining without candidate generation or support constraint.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Frequent pattern mining without candidate generation or support constraint./
Author:	Cheung, William.
Description:	106 p.
Notes:	Source: Masters Abstracts International, Volume: 42-02, page: 0612.
Contained By:	Masters Abstracts International42-02.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ82261
ISBN:	0612822613

Frequent pattern mining without candidate generation or support constraint.
Cheung, William.

Frequent pattern mining without candidate generation or support constraint. - 106 p.

Source: Masters Abstracts International, Volume: 42-02, page: 0612.

Thesis (M.Sc.)--University of Alberta (Canada), 2003.

Mining for frequent patterns in transactional databases has been studied for more than a decade. Many algorithms have been developed to mine static databases. There are a few incremental algorithms, FUP2 and SWF, that allow both addition and deletion of transactions. However, they are not efficient because they have to rescan the whole dataset at least once. They are not suitable in real time situations where transactions are added or deleted constantly and frequent patterns mining could be required at any time.

ISBN: 0612822613Subjects--Topical Terms:

626642
Computer Science.

Frequent pattern mining without candidate generation or support constraint.
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035 $a (UnM)AAIMQ82261
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100 1 $a Cheung, William. $3 1949062
245 1 0 $a Frequent pattern mining without candidate generation or support constraint.
300 $a 106 p.
500 $a Source: Masters Abstracts International, Volume: 42-02, page: 0612.
500 $a Adviser: Osmar Zaiane.
502 $a Thesis (M.Sc.)--University of Alberta (Canada), 2003.
520 $a Mining for frequent patterns in transactional databases has been studied for more than a decade. Many algorithms have been developed to mine static databases. There are a few incremental algorithms, FUP2 and SWF, that allow both addition and deletion of transactions. However, they are not efficient because they have to rescan the whole dataset at least once. They are not suitable in real time situations where transactions are added or deleted constantly and frequent patterns mining could be required at any time.
520 $a In this thesis, we propose a novel data structure called CATS Tree. CATS Tree extends the idea of FP-Tree to improve storage compression and allow frequent pattern mining without generation of candidate itemsets. The proposed algorithms allow mining with a single pass over the database as well as addition or deletion of transactions in the finest granularity at any given time.
590 $a School code: 0351.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 0 $a University of Alberta (Canada). $3 626651
773 0 $t Masters Abstracts International $g 42-02.
790 1 0 $a Zaiane, Osmar, $e advisor
790 $a 0351
791 $a M.Sc.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ82261