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

fYear

2009

fDate

July 29 2009-Aug. 1 2009

Firstpage

133

Lastpage

138

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/SOFA.2009.5254865

Filename

5254865