In this paper, we provide the basic concepts about association rule mining and compared existing algorithms for association rule mining techniques. Of course, a single article cannot describe all the algorithms in detailed, yet we tried to cover the major theoretical issues, which can help the researcher in their researches. KeywordsAssociation rules, algorithm, itemsets, database.

We evaluate the rough set and the association rule method with respect to their performance and the quality of the produced rules. It is shown that despite their different approaches, both methods are based on the same principle and, consequently, must generate identical rules. However, they differ strongly with respect to performance. Subsequently an optimized association rule procedure is pre...

Many algorithms to mine the association rules are divided into two stages, the first is to find the frequent set; the second is use the frequent set to generate association rules. This proposal discuss the respective characteristics and .shortcoming of the current algorithms to mine association rules and propose another method to mine faster; unlike the other algorithms, this algorithm emphasis...

Association rules are logical propositions of the form φ ≈ ψ that are frequently observed in a given set of data. An analyst can express hypotheses in the form of these rules and verify their validity in the data, as well as nd hints to relevant, previously unknown relationships. In this paper, we propose to answer some analytical questions about STULONG, a study of risk factors of atherosclero...

Most of the association rule mining algorithm works based on the assumption that the items present in the dataset are of same kind with similar frequencies. Hence, the algorithms use levelwise support thresholds for mining. When the itemsets are of different frequency and of varied importance, the levelwise support thresholds are not suitable to discover frequent associations. Each item in a le...

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, as numerous measures may be found in the literature, choosing the measures to be applied for a given application is a difficult task. In this chapter, the authors present a novel and useful classification of interestingness measures according to three criteria: the...

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In this paper, we discuss an approach for discovering temporal changes in event sequences, and present rst results from a study on demographic data. The data encode characteristic events in a per-son's life course, such as their birth date, the begin and end dates of their partnerships and marriages, and the birth dates of their children. The goal is to detect signiicant changes in the chronolo...

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GRD is an algorithm for k-most interesting rule discovery. In contrast to association rule discovery, GRD does not require the use of a minimum support constraint. Rather, the user must specify a measure of interestingness and the number of rules sought (k). This paper reports efficient techniques to extend GRD to support mining of negative rules.