A frequent pattern mining algorithm based on FP-growth without generating tree

An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree,...

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Main Authors: Tohidi, Hossein, Ibrahim, Hamidah
Format: Conference or Workshop Item
Language:English
Published: Universiti Utara Malaysia 2010
Online Access:http://psasir.upm.edu.my/id/eprint/59759/
http://psasir.upm.edu.my/id/eprint/59759/1/PG671_676.pdf
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author Tohidi, Hossein
Ibrahim, Hamidah
author_facet Tohidi, Hossein
Ibrahim, Hamidah
author_sort Tohidi, Hossein
building UPM Institutional Repository
collection Online Access
description An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree, which retains the itemset association information. It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately. For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution. In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. Our algorithm works based on prime factorization, and is called Frequent Pattern-Prime Factorization (FPPF).
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spelling upm-597592018-03-22T07:59:31Z http://psasir.upm.edu.my/id/eprint/59759/ A frequent pattern mining algorithm based on FP-growth without generating tree Tohidi, Hossein Ibrahim, Hamidah An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. First, it compresses the database representing frequent items into a frequent-pattern tree, or FPtree, which retains the itemset association information. It then divides the compressed database into a set of conditional databases (a special kind of projected database), each associated with one frequent item or pattern fragment, and mines each such database separately. For a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution. In this paper we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. Our algorithm works based on prime factorization, and is called Frequent Pattern-Prime Factorization (FPPF). Universiti Utara Malaysia 2010 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/59759/1/PG671_676.pdf Tohidi, Hossein and Ibrahim, Hamidah (2010) A frequent pattern mining algorithm based on FP-growth without generating tree. In: Knowledge Management 5th International Conference 2010 (KMICe 2010), 25-27 May 2010, Kuala Terengganu, Terengganu. (pp. 671-676).
spellingShingle Tohidi, Hossein
Ibrahim, Hamidah
A frequent pattern mining algorithm based on FP-growth without generating tree
title A frequent pattern mining algorithm based on FP-growth without generating tree
title_full A frequent pattern mining algorithm based on FP-growth without generating tree
title_fullStr A frequent pattern mining algorithm based on FP-growth without generating tree
title_full_unstemmed A frequent pattern mining algorithm based on FP-growth without generating tree
title_short A frequent pattern mining algorithm based on FP-growth without generating tree
title_sort frequent pattern mining algorithm based on fp-growth without generating tree
url http://psasir.upm.edu.my/id/eprint/59759/
http://psasir.upm.edu.my/id/eprint/59759/1/PG671_676.pdf