Removal of Duplicate Rules for Association Rule Mining from Multilevel Dataset

Association rules are one of the most researched areas of data mining. This is useful in the marketing and retailing strategies. Association mining is to retrieval of a set of attributes shared with a large number of objects in a given database. There are many potential application areas for association rule approach which include design, layout, and customer segregation and so on. The redundancy in association rules affects the quality of the information presented. The goal of redundancy elimination is to improve the quality and usefulness of the rules. Our work aims is to remove hierarchical duplicacy in multi-level, thus reducing the size of the rule set to improve the quality and usefulness without any loss.