(untitled) (International Journal of Engineering and Computer Science, 2016)
– For a data mining process, association rule mining is one of the key components. In the realm of the mining of the data, association rules play a key role to condense interesting correlations, frequent patterns, associations or casual structures from the set of items present in the databases. The current paper focuses on the aspect of the competent mining of the association rules from larger databases. The problem of unearthing of large item sets can be sorted by the creation of an ontology tree. In the domain of instructional design and in the evolution of the course content, ontology plays a very vital part. The understanding about the content can be depicted with the help of ontology trees that in turn would aid the instructors in development of the content and the learners to get permission to use the content in an apt way. Even though ontologies are there for many domains, their fittingness for other subjects is still vague. Further, for many other domains, the ontologies even don’t exist. Many have attempted to devise methods to enhance many dimensions of the ontology, namely, representation languages and inference mechanisms. But, unfortunately very less effort has been taken to improvise the practical results of development method application. In this paper, a discussion on the technique of Association rule mining with ontology (ARMO) is presented that is employed to find the most precise association rules in the area of ontology, ontology analysis, ontology tree and frequent item sets. The spotlight is more on the relationship type that permit one to model rich rules adequately.