As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be us...
Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these...
Anour F. A. Dafa-Alla, Ho-Sun Shon, Khalid E. K. S...
The field of market basket analysis, the search for meaningful associations in customer purchase data, is one of the oldest areas of data mining. The typical solution involves th...
Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multi...
Abstract. We propose a formal definition of the robustness of association rules for interestingness measures. It is a central concept in the evaluation of the rules and has only be...
Yannick Le Bras, Patrick Meyer, Philippe Lenca, St...
This paper aims to find interested association rules, known as data mining technique, out of the dataset of downloading logs by focusing on the coordinated activity among downloadi...
The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usagebased approach to generate recommendations that ...
Data mining evolved as a collection of applicative problems and efficient solution algorithms relative to rather peculiar problems, all focused on the discovery of relevant infor...
Levelwise algorithms (e.g., the Apriori algorithm) have been proved eective for association rule mining from sparse data. However, in many practical applications, the computation ...
It has been pointed out that the usual framework to assess association rules, based on support and confidence as measures of importance and accuracy, has several drawbacks. In part...
Fernando Berzal Galiano, Ignacio J. Blanco, Daniel...