An Efficient Algorithm to Mine Non Redundant Top K Association Rules
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2016, Vol 3, Issue 1
Abstract
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.
Authors and Affiliations
Amardeep Kumar
Keywords
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