In this paper, the mining of single-dimensional association rule and non-repetitive predicate multi-dimensional association rule were integrated. We proposed an algorithm for mining of hybrid-dimensional association rule using a data cube structure. Preliminary result shows that when the number of tuples is large, the algorithm is efficient.

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...

Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Most approaches to association rule mining assume that all items within a dataset have a uniform distribution with respect to support. Therefore, weighted association rule mining (WARM) was introduced to provide a notion of importance to individual items. Previous approac...

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...

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...

Association rules discovery is a fundamental task in spatial data mining where data are naturally described at multiple levels of granularity. ARES is a spatial data mining system that takes advantage from this taxonomic knowledge on spatial data to mine multi-level spatial association rules. A large amount of rules is typically discovered even from small set of spatial data. In this paper we p...

Data mining is the process of extracting the previously unknown patterns from large amount of data. Privacy preserving data mining is one of the research areas in data mining. The main