Association Rules are a basic concept of data mining. They are, however, not understood as logical objects which can be used for reasoning. The purpose of this paper is to investigate a model based semantic for implications with certain constraints on their support and confidence in relational data, which then resemble association rules, and to present a possibility to decide entailment for them.

Nowadays, there are a great number of both specific and general data mining tools available to carry out association rule mining. However, it is necessary to use several of these tools in order to obtain only the most interesting and useful rules for a given problem and dataset. To resolve this drawback, this paper describes a fully integrated framework to help in the discovery and evaluation o...

In data mining, the quality of an association rule can be stated by its support and its confidence. This paper investigates support and confidence measures for spatial and spatio-temporal data mining. Using fixed thresholds to determine howmany times a rule that uses proximity is satisfied seems too limited. It allows the traditional definitions of support and confidence, but does not allow to ...

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommo...

In this paper we describe an approach to classifying objects in a domain where classifications are uncertain using a novel combination of argumentation and data mining. Classification is the topic of a dialogue game between two agents, based on an argument scheme and critical questions designed for use by agents whose knowledge of the domain comes from data mining. Each agent has its own set of...

The aim of the present work is to evaluate differences according to age in digital competence, usages, and attitude towards ICT in a sample of 1231 online students of a distance university. To fulfill this goal, hypothesis testing, correlation analysis, and data mining techniques were performed on the basis of a 72-item survey. Results showed no strong differences between extreme groups of age....

Association mining has been one of the most intensively researched areas of data mining. However, direct uptake of the resulting technologies has been relatively low. This paper examines some of the reasons why the dominant paradigms in association mining have not lived up to their promise, and argues that a powerful alternative is provided by top-k techniques coupled with appropriate statistic...

Data and knowledge hiding are two research directions that investigate how the privacy of raw data, or information, can be maintained either before or after the course of mining the data. By focusing on the knowledge hiding thread, we present a taxonomy and a survey of recent approaches that have been applied to the association rule hiding problem. Association rule hiding refers to the process ...

Data Classification and Association Rule Discovery are two important data mining techniques. In this paper, we apply the two techniques to find appropriate laws and articles for lawsuits. We propose a data classification method using a classifier generated from association rule discovery technique. We utilize the classifier to help selecting laws and articles to be tried for a lawsuit. Our expe...

Data Mining Technique can be used in different fields to extract knowledge from large data. Data Mining is very useful in educational field to find important pattern from the data. The educational institutes are always insists on giving quality education. By using prediction method a model can be developed which can be used to predict students‘ performance. Prediction can be done by using stude...