Review of Modern Technique for Mining Top Ranked Association Rule

In this paper, we propose an efficient algorithm to mine the top-k association rules, where k is the number of association rules to be found and is set by the user. Association mining is a fundamental and well researched data mining technique. However, depending on the choice of the parameters (the minimum support and minimum confidence), current algorithms can become very slow and generate an extremely large amount of results or generate none or too few results, eliding useful information. This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in finding a certain amount of results, and fine tuning the parameters is time-consuming. Experimental and theoretical results indicate that the algorithm is highly effective and has excellent performance and scalability and that it is an advantageous alternative to classical association rule mining algorithms when the user want to control the number of rules generated.