The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a speci c item or rules that contain children of a speci c item in a hierarchy. While such constraints c...

Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. We purpose a Genetic Algorithm to generate high quality Association Rules with four metrics they are confidence, completeness, interestingness and comprehensibility. These metrics are combined as an objective fitness function. Fitness function eva...

over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. The problems of finding frequent item sets are basic in multi level association rule mining, fast algorithms for solving problems are needed. This paper presents an efficient version of apriori algorithm for mining multi-level association rules in large databases to fi...

XML documents have become poplar because the semi-structure nature of XML allows a wide variety of data to be represented in XML. Association rule mining is an important problem in the data mining domain. Currently, the problem of association rule mining on XML data has not been well studied. Existing work only addresses the problem of mining specific association rules from XML data. Such techn...

Association rule mining problem attracts the attention of researchers inasmuch to its importance and applications in our world with the fast growth of the stored data. Association rule mining process is computationally very expensive because rules number grows exponentially as items number in the database increases. However, Association rule mining is more complex when we introduce the quality ...

Discovering association rules from transaction databases is one of the most studied data mining task. Many effective techniques have been proposed over the years. All these algorithms share the same two steps methodology: frequent itemsets enumeration followed by effective association rules generation step. In this paper, we propose a new propositional satisfiability based approach to mine asso...

Rare association rule mining has received a great deal of attention in the recent past. In this research, we use transaction clustering as a pre-processing mechanism to generate rare association rules. The basic concept underlying transaction clustering stems from the concept of large items as defined by traditional association rule mining algorithms. We make use of an approach proposed by Koh ...

Database mining must capture the dynamics of data in the real-world. In other words, mining models would be able to maintain all changed rules when a database is updated. This paper presents a new model of aggregating association rules aimed at not only maintaining association rules in dynamical databases, but also aggregating association rules from di€erent data sources. Indeed, this aggregati...

Uncertain and stochastic states have been always taken into consideration in the fields of risk management and accident, like other fields of industrial engineering, and have made decision making difficult and complicated for managers in corrective action selection and control measure approach. In this research, huge data sets of the accidents of a manufacturing and industrial unit have been st...