A Non Candidate Subset-Superset Dynamic Minimum Support Approach for sequential pattern Mining

Finding frequent patterns in data mining plays a significant role for finding the relational patterns. Data mining is also called knowledge discovery in several database including mobile databases and for heterogeneous environment. In this paper we proposed a modern non candidate Subset-Superset Dynamic minimum support approach for sequential pattern mining. Our Whole procedure is further subdivided in four parts1) Superset with minimum support 2) Subset with minimum support 3) Superset mining with Dynamic Support 4) Subset Mining with Dynamic Support. The entire above block includes 1) Accept the dataset from the input set. 2) Generate Token Based on the character, we only generate posterior tokens. 3) Minimum support is entering by the user according to the need and place. 4) Find the frequent pattern which is according to the dynamic minimum support 5) Find associated member according to the token value 6) Find useful pattern after applying pruning. In this approach we also find improved association, which shows that which item set is most acceptable association with others. Here we also provide the flexibility to find multiple minimum supports which is useful for comparison with associated items and dynamic support range. Our algorithm provides the flexibility for improved association and dynamic support. Comparative result shows the effectiveness of our algorithm.