Abstract
We explore in this paper a practicably interesting mining task to retrieve frequent itemsets with memory constraint. As opposed to most previous works that concentrate on improving the mining efficiency or on reducing the memory size by best effort, we first attempt to constrain the upper memory size that can be utilized by mining frequent itemsets in this paper.
| Original language | English |
|---|---|
| Title of host publication | CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management |
| Pages | 345-346 |
| Number of pages | 2 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management - Bremen, Germany Duration: 2005 Oct 31 → 2005 Nov 5 |
Other
| Other | CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management |
|---|---|
| Country | Germany |
| City | Bremen |
| Period | 05-10-31 → 05-11-05 |
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
Cite this
}
Frequent pattern discovery with memory constraint. / Chuang, Kun-Ta; Chen, Ming Syan.
CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management. 2005. p. 345-346.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Frequent pattern discovery with memory constraint
AU - Chuang, Kun-Ta
AU - Chen, Ming Syan
PY - 2005
Y1 - 2005
N2 - We explore in this paper a practicably interesting mining task to retrieve frequent itemsets with memory constraint. As opposed to most previous works that concentrate on improving the mining efficiency or on reducing the memory size by best effort, we first attempt to constrain the upper memory size that can be utilized by mining frequent itemsets in this paper.
AB - We explore in this paper a practicably interesting mining task to retrieve frequent itemsets with memory constraint. As opposed to most previous works that concentrate on improving the mining efficiency or on reducing the memory size by best effort, we first attempt to constrain the upper memory size that can be utilized by mining frequent itemsets in this paper.
UR - http://www.scopus.com/inward/record.url?scp=33745771618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33745771618&partnerID=8YFLogxK
U2 - 10.1145/1099554.1099659
DO - 10.1145/1099554.1099659
M3 - Conference contribution
SN - 1595931406
SN - 9781595931405
SP - 345
EP - 346
BT - CIKM'05 - Proceedings of the 14th ACM International Conference on Information and Knowledge Management
ER -