An Efficient Algorithm to Mine Non Redundant Top K Association Rules

Association rule mining is employed the foremost well-liked fiction within the field of analysis of knowledge
mining. This paper presents a survey of some commonest techniques, that square measure often used for
mining association rules from a knowledge set.
Association mining may be a cardinal and advantageous researched data processing proficiency. However,
looking on the choice of the arguments (the minimum support and minimum confidence), current algorithms
will become terribly slow associated generate an exceptional large quantity of results or generate none or too
few results, eliding helpful data, as a results of in apply users have restricted resources for analyzing the
results and thus square measure usually only fascinated by discovering a particular amount of results, and fine
standardization the parameters is time overwhelming. To handle this disadvantage, we tend to tend to propose
associate formula to mine the top-k association rules, where k is that the variability of association rules to be
found and is ready by the user. The formula utilizes a replacement approach for generating association rules
named rule growths and includes several optimizations experimental results show that the formula has
marvelous performance and quantify ability that it's associate advantageous completely different to classical
association rule mining algorithms once the user would like to manage the number of rules generated.