Association rules are interesting correlations among attributes in a database. These rules have many applications in areas ranging from e-commerce to sports to census analysis to medical diagnosis. The discovery of association rules is an extremely computationally expensive task and it is therefore imperative to have fast scalable algorithms for mining these rules. In this thesis, we present eÆ...

In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system, named ARES that takes advantage of the use of a multi-relational approach to mine spatial association rules. It supports spatial database coupling and discovery of multi-level spatial association rules as a means for spatial data exploration. We also prese...

Brands are increasingly using social media to create and manage posts initiate maintain consumer engagement. Based on the theory of engagement, a perspective brand post content, form posting time is introduced construct conceptual model engagement for Sina Weibo. Rough set method reduction algorithm Holte 1 R used automatically generate optimal decision rules. does not need any prior knowledge ...

Data mining is a process of discovering patterns and relationships in data with the help of various data analysis tools, to make valid predictions. Association rule learning which finds the relationships between the variables. Association rules are important features for image classification, mining and rational selection to obtain accurate classification. In this paper, it is an approach to pr...

Association rule mining is interesting area of data mining research which discovers correlations between different item sets in a transaction database. Efforts have been made for efficient hiding of sensitive association rules, but these techniques do not consider the consequences such as loss of information, lost rules and increase in ghost rules production. In this paper, we propose improved ...

This paper proposes an anticipation model of potential customers’ purchasing behavior. This model is inferred from past purchasing behavior of loyal customers and the web server log files of loyal and potential customers by means of clustering analysis and association rules analysis. Clustering analysis collects key characteristics of loyal customers’ personal information; these are used to loc...