An Ontological Approach for Mining Association Rules from Transactional Dataset

Infrequent item sets are mined in order to reduce the cost function and to make the sale of a rare data correlated\ud item set. In the past research, algorithms like Infrequent Weighted Item Set Miner and Minimal Infrequent\ud Weighted Item Set Miner were used. Since, mining of infrequent item set is done by satisfying support count\ud less than or equal to the maximum support count many number of rules were generated and the mined result do\ud not guarantee that only interesting rules were extracted, as the interestingness is strongly depends on the user\ud knowledge and goals. Hence, an Ontology Relational Weights Measure using Weighted Association Rule\ud Mining approach is introduced to integrate user’s knowledge, minimize number of rules and mine the interesting\ud infrequent item sets