A novel concept for mining negative and positive rule through association based k-map

The association rule is used to find the items enumerated in the transactions. These rules are known as positive and negative rules. Many researchers have focused on positive association rule mining and developed many algorithms for finding the frequent item sets from the large databases however few have come up with the concept of negative association rule mining and therefore the lesser number of algorithms is developed in this area. This paper puts forward the novel concept of association rule mining using k-map,for mining the positive and negative frequent patterns in the domain of web usage which is an integration of two different areas (data mining and internet). This novel approach drastically reduces the number of scans that are generated by algorithms like Apriori and scans the entire database just once and hence results in fast execution. © School of Engineering,Taylor’s University.