Compressed Theory for Mining Frequent Patterns Using Graphical Technique

Exploring association rules, correlations, clusters and many more theories that make out interactions amongst set of items is frequently studied in data mining research, involves complications in mining the data. Mining from the scratch is the most time consuming operation in this discovery process in the computation of the frequency of occurrences of subset of items in the database of transaction, since the size of each set of transaction may be massive that it makes difficult to perform traditional data mining tasks. This research intends to propose a graph structure that captures only those itemsets that needs to define a sufficiently immense dataset into a submatrix representing important weights and does not give any chance to outliers. We have devised a strategy that covers significant facets of data by drilling down the large data into a succinct form of an Adjacency Matrix at different stages of mining process. The graph structure is so designed that it can be easily maintained and the trade off in compressing the large data values is reduced. This proposed research is an explorative way for regular frequent itemsets with fewer scans.