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Data Mining And Association Rule Mining

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2. Background and Literature Review
The purpose of this chapter is to show an in depth review of the topics, areas and works related to the research presented here. we conduct a brief but comprehensive in depth review of data mining and association rule mining approaches and techniques, followed by a focus at interestingness & quality, and redundancy issues related to association rule mining. This review sets the basic work for our research and the proposals made here.
2.1 Data mining
Data Mining technique is the result of a long process of studies and research in the area of databases and product development. This evolution began when business data and companies was stored for the first time on computer device, with continuous …show more content…

2.2 Frequent Pattern Mining
In this part, we introduce frequent pattern mining by giving an overview of the problem, including a formal definition, the description of some practical applications and a survey of the most renowned and influential algorithms proposed for solving this problem.
2.2.1 Overview
Frequent pattern mining and association rule mining were first introduced in 1993 by Agrawal et al. [15]. Informally speaking, association rules can be seen as if-then rules: e.g. if a person buys cheese, he or she also buys beer. A measure that is often associated with association rule mining is that of support: the some of customers for whom the rule holds, or rather the relative number of customers buying all items occurring in the rule (the so-called underlying pattern or itemset) [16]. Basically, the objective is to find those items in a data set that commonly co-occur, based on a certain minimum support value. Besides itemsets, it 's also possible to mine more complex patterns, such as trees and graphs.
2.2.2 Definition The formal definition of frequent pattern mining and association rule mining in a relational setting is the following:
Let db be a transaction table with schema R = {I1,I2……. In}, in which each Ii is a binary attribute. The attributes in db correspond to items and the rows in db correspond to

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