A Partition Model for Multilevel Association Rule Mining

We have extended the capacity of the learn of mining association rules from single level to multiple concept levels and studied methods for mining multiple-level association rules from large transaction databases. Mining multiple-level association rules may lead to progressive mining of refined knowledge from data and have interesting applications for knowledge discovery in transaction databases, as well as other business or engineering databases. Mining frequent patterns in huge transactional database is an extremely researched area in the field of data mining. Mining frequent itemsets is a basic problem for mining association rules. Taking out association rules at multiple levels helps in discovers more specific and applicable knowledge. Even as computing the number of occurrence of an item we require to scan the given database lots of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper a new approach is introduced for solving the above mentioned issues. Therefore this algorithm is above all fit for very large size databases. We also use a top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. This method first finds frequent data items at the topmost level and then progressively deepens the mining process into their descendants at lower concept levels.