The paper presents the problem of the unsupervised discretization of continuous attributes for association rules mining. It shows commonly used techniques for this aim and highlights their principal limitations. To overcome such limitations a method based on the use of a SOM is presented and tested over various real world datasets.

the association rule into classification can improve the accuracy and obtain some valuable rules and information that cannot be captured by other classification approaches. However, the rule generation procedure is very time-consuming when encountering large data set. Besides, traditional classifier building is organized in several separate phases which may also degrade the efficiency of these ...

This work describes our experiences on discovering association rules in medical data to predict heart disease. We focus on two aspects in this work: mapping medical data to a transaction format suitable for mining association rules and identifying useful constraints. Based on these aspects we introduce an improved algorithmto discover constrained association rules. We present an experimental se...

Category learning is thought to be mediated—in at least some category structures—by hypothesis-testing processes. Verbal labels for the stimuli and stimulus individuation have been shown to facilitate the formation, testing, and application of rules of category membership (Fotiadis & Protopapas, 2014). We sought to replicate the phenomenon of facilitation due to verbal names for the stimuli by ...

  The unprecedented growth of competition in the banking technology has raised the importance of retaining current customers and acquires new customers so that is important analyzing Customer behavior, which is base on bank databases. Analyzing bank databases for analyzing customer behavior is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily t...

We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (filters) on the associations to be generated. Our approach is a combination of the incorporation of filtering conditions inside the mining phase, and the filtering of already generated associations. We present several concrete algorithms and compare ...

Predicting the next element(s) of a sequence is a research problem with wide applications such as stock market prediction, consumer product recommendation, and web link recommendation. To address this problem, an effective approach is to mine sequential rules from a set of training sequences to then use these rules to make predictions for new sequences. In this paper, we improve on this approac...