Association rules shows us interesting associations among data items. It means that an association rule clearly defines that how a data item is related or associated with another data item. That is why these types of rules are called Association rules. And the procedure by which these rules are extracted and managed is known as Association rule mining. Classical association rule mining had many...

This paper is a literature review of a new algorithm for mining association rules in a spatial database. Spatial data mining, or knowledge discovery in large spatial databases, is the process of extracting implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases. Recently, there has been a lot of research in data mining and these studies led to a set o...

Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of discovery is not pre-determined, while for classification rule mining there is one and only one pre...

Each semester, students go through the process of selecting appropriate courses. It is difficult to find information about each course and ultimately make decisions. The objective of this paper is to design a course recommender model which takes student characteristics into account to recommend appropriate courses. The model uses clustering to identify students with similar interests and skills...

Two important constraints of association rule mining algorithm are support and confidence. However, such constraints-based algorithms generally produce a large number of redundant rules. In many cases, if not all, number of redundant rules is larger than number of essential rules, consequently the novel intention behind association rule mining becomes vague. To retain the goal of association ru...

Mining association rules is one of the most important tasks in data mining. The classical model of association rules mining is supportconfidence. The support-confidence model concentrates only on the existence or absence of an item in transaction records and does not take into account the products’ prices and quantities and how such these detailed information can affect the overall performance ...

The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the disco...

Mining association rules in the database is one of important study in data mining research. Traditional association rules consist of some redundant information, and need scan database many times and generate lots of candidate item sets. Aiming at low efficiency in association rules mining using traditional methods, this paper proposes the algorithm (ISMFP), which is based on intersection for mi...

The goal of this extended abstract is to contribute to the forum for research on construction of data mining workflows. We briefly introduce a formal framework called FOFRADAR (FOrmal FRAmework for Data mining with Association Rules) and then we outline how it can be used to control a workflow of data mining with association rules. We consider this relevant to associative classifiers that use a...