In this paper we give an overview of the continued development of interestingness measures and how they have evolved into the two main techniques of objective and subjective measures of interest. The objective approach uses the statistical strength or characteristics of the patterns to assess their degree of interestingness. Subjective techniques incorporate the user’s subjective knowledge into the assessment strategy. Association rule mining is a popular data mining method available in R as the extension package a rules. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. In existing system, the system use fuzzy formal concept analysis. Formal Concept Analysis (FCA) is a data analysis technique based on the ordered lattice theory. It defines formal contexts to represent relationships between objects and attributes in a domain and interprets the corresponding concept lattice. The concept lattice is more informative than traditional treelike conceptual structures as it can also support multiple inheritances. This makes FCA a very suitable technique for conceptual clustering. The existing system had some drawbacks, so the system proposed a recently proposed pruning rule for hierarchical search algorithms based on the use of precomputed distances between nodes in the tree and the objects of the training set has been explored and compared with some state of the art methods. In this work some proposals to reduce the size of the table have also been presented. Recently a new pruning rule for hierarchical similarity search algorithms based on the use of a distance table was proposed.