Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

14

EDBT
2009
ACM

favoriteEmaildiscussreport

109views Database» more EDBT 2009»

Estimating the number of frequent itemsets in a large database

14 years 2 months ago

Estimating the number of frequent itemsets in a large database

Download www.kent.edu

Ruoming Jin, Scott McCallen, Yuri Breitbart, David

Real-time Traffic

Database | EDBT 2009 |

claim paper

Related Content

» An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream

» New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases

» MINI Mining Informative Nonredundant Itemsets

» FIT A Fast Algorithm for Discovering Frequent Itemsets in Large Databases

» A Treebased Approach for Efficiently Mining Approximate Frequent Itemsets

» LCM ver 2 Efficient Mining Algorithms for FrequentClosedMaximal Itemsets

» Verifying and Mining Frequent Patterns from Large Windows over Data Streams

» Efficient discovery of errortolerant frequent itemsets in high dimensions

» Generalized Closed Itemsets for Association Rule Mining

Post Info
More Details (n/a)

Added	19 May 2010
Updated	19 May 2010
Type	Conference
Year	2009
Where	EDBT
Authors	Ruoming Jin, Scott McCallen, Yuri Breitbart, David Fuhry, Dong Wang

Comments (0)