In this digital age, organizations have to deal with huge amounts of data, sometimes called Big Data. In recent years, the volume of data has increased substantially. Consequently, finding efficient and automated techniques for discovering useful patterns and relationships in the data becomes very important. In data mining, patterns and relationships can be represented in the form of association rules. Current techniques for discovering association rules rely on measures such as support for finding frequent patterns and confidence for finding association rules. A shortcoming of confidence is that it does not capture the correlation that exists between the left-hand side (LHS) and the right-hand side (RHS) of an association rule. On the other hand, the interestingness measure lift captures such as correlation in the sense that it tells us whether the LHS influences the RHS positively or negatively. Therefore, using Lift instead of confidence as a criteria for discovering association rules can be more effective. It also gives the user more choices in determining the kind of association rules to be discovered. This in turn helps to narrow down the search space and consequently, improves performance. In this paper, we describe a new approach for discovering association rules that is based on Lift and not based on confidence.
Traditionally, research in the area of frequent itemset mining has focused on mining market basket data. Several algorithms and techniques have been introduced in the literature for mining data represented in basket data format. The primary objective of these algorithms has been to improve the performance of the mining process. Unlike basket data representation, no algorithms exist for mining frequent itemsets and association rules in relational databases that are represented using the formal relational data model. Typical relational data can not be easily converted to basket data representation for the purpose of applying frequent itemset mining algorithms. Therefore, a need arises for algorithms that can directly be applied to data represented using the formal relational data model and for a conceptual framework for mining such data. This paper solves this problem by introducing an algorithm named RDB-MINER for mining frequent itemsets in relational databases.
Abstract-Traditionally, software design and development has been following the engineering approach as exemplified by the waterfall model, where specifications have to be fully detailed and agreed upon prior to starting the software construction process. Agile software development is a relatively new approach in which specifications are allowed to evolve even after the beginning of the development process, among other characteristics. Thus, agile methods provide more flexibility than the waterfall model, which is a very useful feature in many projects. To benefit from the advantages provided by agile methods, the adoption rate of these methods in software development projects can be further encouraged if certain practices and techniques in agile methods are improved. In this paper, an analysis is provided of several practices and techniques that are part of agile methods that may hinder their broader acceptance. Further, solutions are proposed to improve such practices and consequently facilitate a wider adoption rate of agile methods in software development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.