Computational Model for Mining Frequent Sets in Large Databases

In this paper we have addressed the problem that we overcome in data mining applications i.e. mining frequent patterns in large databases efficiently in less time with less memory requirement. We are proposing a computational model for finding frequent patterns in large datasets with less number of scans .We have also proposed methodology which would help in storing large database compactly and thus help in improving the storage space requirement. Our model would help in generating less patterns and thus improving the mining time required in large databases. Our computational model is an amalgamation of three approaches i.e. bottom up counting inference and top down intersection method for generating the frequent sets and tree based approach for storing the databases compactly