IncSpan :Incremental Mining of Sequential Patterns in Large Database. Hong Cheng , Xifeng Yan , Jiawei Han Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04) Advisor ： Jia-Ling Koh
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IncSpan :Incremental Mining of Sequential Patterns in Large Database
Hong Cheng, Xifeng Yan , Jiawei Han
Proc. 2004 Int. Conf. on Knowledge Discovery and Data Mining (KDD'04)
(1) INSERT :inserting new sequences
(2) APPEND: appending new itemsets/items to the existing sequences
(newly purchased items for existing customers)
,it might be frequent in
and APPEND, we can treat INSERT as a special case of APPEND – treating the inserted sequences as appended transactions to an empty sequence in the original database.
Examples in INSERT and APPEND database
If supLDB(p) < (1 - )*min_sup, then
supLDB(p’ )supLDB(p) < (1 - )*min_sup
Since supLDB(p’ ) = supODB(p’ ) + sup(p’ ).
Then we have sup(p’ )supLDB(p’ ) < (1 - )*min_sup.(2)
Since sup (p’ ) = sup (p’) + sup(p’), combining (1)and (2), we have sup (p’) < min_sup. So p’ cannot be frequent in
adjust the support of those patterns.
(a) varying min sup
(b) varying percentage of updated sequences
(c) Memory Usage under varied min sup
(a) varying buffer ratio
(b) multiple increments of
(c) varying # of sequences (in
1000) in DB