TY  - JOUR
T1  - Efficient Graph Structure for the Mining of Frequent Itemsets From Data Streams
AU - , E.R. Naganthan AU - , F. Ramesh Dhanaseelan 
JO  - International Journal of Soft Computing
VL  - 3
IS  - 2
SP  - 144
EP  - 146
PY  - 2008
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2008.144.146
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2008.144.146
KW  - feature extraction
KW  -data stream
KW  -association rule mining
KW  -frequent itemsets
AB  - In this study, we propose a graph structure, that captures important data streams. This graph can be easily maintained and mined for frequent item sets as well as various other patterns like constrained item sets. This graph captures the contents of transaction in a window and arranges nodes according to some canonical order that is unaffected by changes in item frequency. This graph structure is designed for exact stream mining of regular frequent item sets.
ER  -