TY - GEN
T1 - Rule mining and missing-Value prediction in the presence of data ambiguities
AU - Wickramaratna, Kasun
AU - Kubat, Miroslav
AU - Premaratne, Kamal
AU - Wickramarathne, Thanuka
PY - 2009
Y1 - 2009
N2 - The success of knowledge discovery in real-world domains often depends on our ability to handle data imperfections. Here we study this problem in the framework of association mining, seeking to identify frequent itemsets in transactional databases where the presence of some items in a given transaction is unknown. We want to use the frequent itemsets to predict "missing items": based on the partial contents of a shopping cart, predict what else will be added. We describe a technique that addresses this task, and report experiments illustrating its behavior.
AB - The success of knowledge discovery in real-world domains often depends on our ability to handle data imperfections. Here we study this problem in the framework of association mining, seeking to identify frequent itemsets in transactional databases where the presence of some items in a given transaction is unknown. We want to use the frequent itemsets to predict "missing items": based on the partial contents of a shopping cart, predict what else will be added. We describe a technique that addresses this task, and report experiments illustrating its behavior.
UR - http://www.scopus.com/inward/record.url?scp=70350514484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350514484&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:70350514484
SN - 9781577354192
T3 - Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
SP - 361
EP - 366
BT - Proceedings of the 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
T2 - 22nd International Florida Artificial Intelligence Research Society Conference, FLAIRS-22
Y2 - 19 March 2009 through 21 March 2009
ER -