RARE: mining colossal closed itemset in high dimensional data

The present society has been sculpted into a continuous data generator. In fact, the massive automatic data collection has generated a new genre of dataset, termed as ‘highdimensional data’, which is characterized by a relatively small number of rows, in comparison to that of large number of columns...

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Main Authors:	Md Zaki, Fatimah Audah, Zulkurnain, Nurul Fariza
Format:	Article
Language:	English English English
Published:	Elsevier B.V. 2018
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/65106/ http://irep.iium.edu.my/65106/ http://irep.iium.edu.my/65106/ http://irep.iium.edu.my/65106/24/65106_RARE-%20mining%20colossal%20closed.pdf http://irep.iium.edu.my/65106/13/65106_Mining%20colossal%20closed%20itemset%20in%20high%20dimensional%20data_SCOPUS.pdf http://irep.iium.edu.my/65106/18/65106%20RARE_Mining%20colossal%20closed%20itemset%20WOS.pdf

id	iium-65106
recordtype	eprints
spelling	iium-651062019-03-14T04:41:48Z http://irep.iium.edu.my/65106/ RARE: mining colossal closed itemset in high dimensional data Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza TK7885 Computer engineering The present society has been sculpted into a continuous data generator. In fact, the massive automatic data collection has generated a new genre of dataset, termed as ‘highdimensional data’, which is characterized by a relatively small number of rows, in comparison to that of large number of columns (or dimensions). Among the vast data mining tasks, association rules have been extensively employed so as to describe the correlations between the variables found in a dataset. The task of mining association rules highly relies on the efficiency of the algorithms to extract all frequent itemsets that exist in the database. The focus towards improving run time and memory consumption of algorithms is strongly influenced by search strategies, effective pruning strategies, and the method of closure checking. Neither depth- nor breadth-first search may exert any variance without these techniques, mainly because the search space appears similar. With that, this paper investigated the strategies implemented in both row and column enumerationbased algorithms, hence proposing the RARE; a breadth-first bottom-up row-enumeration algorithm, in mining colossal closed itemsets in high-dimensional data Elsevier B.V. 2018-12-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/65106/24/65106_RARE-%20mining%20colossal%20closed.pdf application/pdf en http://irep.iium.edu.my/65106/13/65106_Mining%20colossal%20closed%20itemset%20in%20high%20dimensional%20data_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/65106/18/65106%20RARE_Mining%20colossal%20closed%20itemset%20WOS.pdf Md Zaki, Fatimah Audah and Zulkurnain, Nurul Fariza (2018) RARE: mining colossal closed itemset in high dimensional data. Knowledge-Based Systems, 161. pp. 1-11. ISSN 0950-7051 E-ISSN 1872-7409 https://www.sciencedirect.com/science/article/abs/pii/S0950705118303769 10.1016/j.knosys.2018.07.025
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza RARE: mining colossal closed itemset in high dimensional data
description	The present society has been sculpted into a continuous data generator. In fact, the massive automatic data collection has generated a new genre of dataset, termed as ‘highdimensional data’, which is characterized by a relatively small number of rows, in comparison to that of large number of columns (or dimensions). Among the vast data mining tasks, association rules have been extensively employed so as to describe the correlations between the variables found in a dataset. The task of mining association rules highly relies on the efficiency of the algorithms to extract all frequent itemsets that exist in the database. The focus towards improving run time and memory consumption of algorithms is strongly influenced by search strategies, effective pruning strategies, and the method of closure checking. Neither depth- nor breadth-first search may exert any variance without these techniques, mainly because the search space appears similar. With that, this paper investigated the strategies implemented in both row and column enumerationbased algorithms, hence proposing the RARE; a breadth-first bottom-up row-enumeration algorithm, in mining colossal closed itemsets in high-dimensional data
format	Article
author	Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza
author_facet	Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza
author_sort	Md Zaki, Fatimah Audah
title	RARE: mining colossal closed itemset in high dimensional data
title_short	RARE: mining colossal closed itemset in high dimensional data
title_full	RARE: mining colossal closed itemset in high dimensional data
title_fullStr	RARE: mining colossal closed itemset in high dimensional data
title_full_unstemmed	RARE: mining colossal closed itemset in high dimensional data
title_sort	rare: mining colossal closed itemset in high dimensional data
publisher	Elsevier B.V.
publishDate	2018
url	http://irep.iium.edu.my/65106/ http://irep.iium.edu.my/65106/ http://irep.iium.edu.my/65106/ http://irep.iium.edu.my/65106/24/65106_RARE-%20mining%20colossal%20closed.pdf http://irep.iium.edu.my/65106/13/65106_Mining%20colossal%20closed%20itemset%20in%20high%20dimensional%20data_SCOPUS.pdf http://irep.iium.edu.my/65106/18/65106%20RARE_Mining%20colossal%20closed%20itemset%20WOS.pdf
first_indexed	2023-09-18T21:32:22Z
last_indexed	2023-09-18T21:32:22Z
_version_	1777412595836256256

RARE: mining colossal closed itemset in high dimensional data

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