Mining frequent sequences using itemset-based extension
dc.contributor.author | Ma, Zhixin | |
dc.contributor.author | Xu, Yusheng | |
dc.contributor.author | Dillon, Tharam S. | |
dc.contributor.author | Chen, Xiaoyun | |
dc.contributor.editor | Craig Douglas | |
dc.contributor.editor | Ping-Kong Alexander Wai | |
dc.date.accessioned | 2017-01-30T11:10:16Z | |
dc.date.available | 2017-01-30T11:10:16Z | |
dc.date.created | 2009-03-25T18:01:42Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Ma, Zhixin and Xu, Yusheng and Dillon, Tharam S. and Chen, Xiaoyun. 2008. Mining frequent sequences using itemset-based extension, in Craig Douglas and Ping-Kong Alexander Wai (ed), International MultiConference of Engineers and Computer Scientists (IMECS 2008), Mar 19 2008, pp. 591-596.Hong Kong: IAENG | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/9047 | |
dc.description.abstract |
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. | |
dc.publisher | IAENG | |
dc.subject | data mining algorithms | |
dc.subject | Frequent sequence mining | |
dc.subject | frequent pattern | |
dc.subject | sequence database | |
dc.title | Mining frequent sequences using itemset-based extension | |
dc.type | Conference Paper | |
dcterms.source.startPage | 591 | |
dcterms.source.endPage | 596 | |
dcterms.source.title | Proceedings of the international multiconference of engineers and computer scientists (IMECS 2008) | |
dcterms.source.series | Proceedings of the international multiconference of engineers and computer scientists (IMECS 2008) | |
dcterms.source.isbn | 9789889867188 | |
dcterms.source.conference | International MultiConference of Engineers and Computer Scientists (IMECS 2008) | |
dcterms.source.conference-start-date | Mar 19 2008 | |
dcterms.source.conferencelocation | Hong Kong | |
dcterms.source.place | Hong Kong | |
curtin.note |
The link to the International MultiConference of Engineers and Computer Scientists (IMECS 2008) is : | |
curtin.department | Centre for Extended Enterprises and Business Intelligence | |
curtin.accessStatus | Open access | |
curtin.faculty | Curtin Business School | |
curtin.faculty | The Centre for Extended Enterprises and Business Intelligence (CEEBI) |