Mining frequent sequences using itemset-based extension
In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we pre...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IAENG
2008
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/9047 |
| _version_ | 1848745836766298112 |
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| author | Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun |
| author2 | Craig Douglas |
| author_facet | Craig Douglas Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun |
| author_sort | Ma, Zhixin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm. |
| first_indexed | 2025-11-14T06:23:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-9047 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:23:42Z |
| publishDate | 2008 |
| publisher | IAENG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-90472022-11-21T05:19:40Z Mining frequent sequences using itemset-based extension Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun Craig Douglas Ping-Kong Alexander Wai data mining algorithms Frequent sequence mining frequent pattern sequence database In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm. 2008 Conference Paper http://hdl.handle.net/20.500.11937/9047 IAENG fulltext |
| spellingShingle | data mining algorithms Frequent sequence mining frequent pattern sequence database Ma, Zhixin Xu, Yusheng Dillon, Tharam S. Chen, Xiaoyun Mining frequent sequences using itemset-based extension |
| title | Mining frequent sequences using itemset-based extension |
| title_full | Mining frequent sequences using itemset-based extension |
| title_fullStr | Mining frequent sequences using itemset-based extension |
| title_full_unstemmed | Mining frequent sequences using itemset-based extension |
| title_short | Mining frequent sequences using itemset-based extension |
| title_sort | mining frequent sequences using itemset-based extension |
| topic | data mining algorithms Frequent sequence mining frequent pattern sequence database |
| url | http://hdl.handle.net/20.500.11937/9047 |