Title :
Market-basket problem solved with depth first multi-level apriori mining algorithm
Author :
Pater, Mirela ; Popescu, Daniela E.
Author_Institution :
Dept. of Comput. Sci., Univ. of Oradea, Oradea, Romania
fDate :
July 29 2009-Aug. 1 2009
Abstract :
The problem of deriving association rules from data was first formulated in [9] and is called the ldquomarket-basket problemrdquo. This paper presents an efficient version of apriori algorithm for mining multi-level association rules in large databases to solve market-basket problem. Our algorithm, named depth first multi-level apriori (DFMLA), uses the benefits of multi-leveled databases, by using the information gained by studying items from one concept level for the study of the items from the following concept levels.
Keywords :
data mining; very large databases; DFMLA; association rule mining; depth first multilevel apriori mining algorithm; large database; market-basket problem; multileveled database; Association rules; Computer science; Data engineering; Data mining; Electronic mail; Information management; Information retrieval; Information technology; Itemsets; Transaction databases; data mining; knowledge discovery in databases; multi-level association rules mining; multi-level databases; support constrains;
Conference_Titel :
Soft Computing Applications, 2009. SOFA '09. 3rd International Workshop on
Conference_Location :
Arad
Print_ISBN :
978-1-4244-5054-1
Electronic_ISBN :
978-1-4244-5056-5
DOI :
10.1109/SOFA.2009.5254865
