In this paper we study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and χ are compared with confidence, Laplace, mutual information, cosine, Jaccard and φ-coefficient. As prediction models, we use sets of association rules generated as such. Classification is done by selecting the best rule, or by ...

Generalized association rules are rules that contain some background knowledge, therefore, giving a more general view of the domain. This knowledge is codified by a taxonomy set over the data set items. Many researches use taxonomies in different data mining steps to obtain generalized rules. In general, those researches reduce the obtained set by pruning some specialized rules using a subjecti...

On-line analytical processing (OLAP) provides tools to explore data cubes in order to extract interesting information. Nevertheless, it cannot offer any explanation of relationships that could exist within data. To achieve this goal, the association rules were performed on data cubes. We focus in this work on a particular class of association rules which is the cyclic association rules. The lat...

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. We start this paper with presenting a simple probabilistic framework for transaction data which can b...

Data mining is an area of data analysis that has arisen in response to new data analysis challenges, such as those posed by massive data sets or non-traditional types of data. Association analysis, which seeks to find patterns that describe the relationships of attributes (variables) in a binary data set, is an area of data mining that has created a unique set of data analysis tools and concept...

In this paper we present a new model, designated as Association Graph, to improve document representation, facilitating the ontological dimension. We explain how to generate and use this kind of graph. Also, we analyze different document similarity measures based on this representation. A classical vector space model was used to evaluate this model and measures, investigating their strengths an...

Interestingness measures provide information that can be used to prune or select association rules. A given value of an interestingness measure is often interpreted relative to the overall range of the values that the interestingness measure can take. However, properties of individual association rules restrict the values an interestingness measure can achieve. An interesting measure can be sta...

The study of association rules within groups of individuals in a database is interesting to define their characteristics and their behavior. In this paper, we define group association rules and we study interestingness measures for them. These evaluation measures can be used to rank groups of individuals and also rules within each group.

a central aim of educational research in the area of mathematical modeling and applications is to recognize the attainment level of students at defined states of the modeling process. in this paper, we introduce principles of fuzzy sets theory and possibility theory to describe the process of mathematical modeling in the classroom. the main stages of the modeling process are represented as fuzz...