Treap mining – A comparison with traditional algorithm

Abstract

In this era of big data analysis, mining results hold a very important role. So, the data scientists need to be accurate enough with the tools, methods and procedures while performing rule mining. The major issues faced by these scientists are incremental mining and the huge amount of time that is virtually required to finish the mining task. In this context, we propose a new rule mining algorithm which mines the database in a priority based model for finding interesting relations. In this paper a new mining algorithm using the data structure Treap is explained along with its comparison with the traditional algorithms. The proposed algorithm finishes the task in O (n) in its best case analysis and in O (n log n) in its worst case analysis. The algorithm also considers less frequent high priority attributes for rule creation, thus making sure to create valid mining rules. Thus the major issues of traditional algorithms like creating invalid rules, longer running time and high memory utilization could be remedied by this new proposal. The algorithm was tested against various datasets and the results were evaluated and compared with the traditional algorithm. The results showed a peak performance improvement.