A Level Wise Tree Based Approach for Ontology-Driven Association Rules Mining

In many database applications, information stored in a database has a built-in hierarchy consisting of multiple levels of concepts. This is also called ontological driven data. The problems of finding frequent item sets are basic in multi level association rule mining and fast algorithms for solving problems are needed. This paper presents an efficient version of FP-Growth algorithm for mining multi-level (FPGM:FP-Growth in Multilevel) association rules in large databases. The proposed method is designed in such a way that it can find maximum frequent itemset at lower level of abstraction in ontological categorical data. The proposed model adopts a top down progressively deepening approach to derive large itemsets. Proposed algorithm works well comparison with general approach of multilevel association rules in term of execution time and throughput. Proposed algorithm is flexible in term of supply different support value for different level. This paper discus some important and crucial issue regarding support value and dataset. There are also some special cases discussed. These cases reveal the behavior of proposed algorithm in different circumstances.An example is also given to demonstrate and support that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.