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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English English |
| Published: |
Elsevier B.V.
2018
|
| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848786330329284608 |
|---|---|
| author | Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza |
| author_facet | Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza |
| author_sort | Md Zaki, Fatimah Audah |
| building | IIUM Repository |
| collection | Online Access |
| 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 |
| first_indexed | 2025-11-14T17:07:19Z |
| format | Article |
| id | iium-65106 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T17:07:19Z |
| publishDate | 2018 |
| publisher | Elsevier B.V. |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 |
| spellingShingle | TK7885 Computer engineering Md Zaki, Fatimah Audah Zulkurnain, Nurul Fariza RARE: mining colossal closed itemset in high dimensional data |
| title | 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_short | RARE: mining colossal closed itemset in high dimensional data |
| title_sort | rare: mining colossal closed itemset in high dimensional data |
| topic | TK7885 Computer engineering |
| 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 |