An Efficient Association Rule Mining Algorithm Based on Animal Migration Optimization Processing of Unknown Incidents in Crime Analysis Brance

A new protection scheme was proposed to avoid this problem. It is intended that each node uses a salted and a not salted HLL. If their estimates differ considerably, an attacker attempts to manipulate the estimates of HLL. In addition to avoiding manipulation, the proposed salted and unsalted (SNS) regime can also detect attempts at manipulation. A practical configuration showing how manipulation attempts can be detected in a low false positive probability has been shown to be applicable to this SNS scheme. Therefore, if merge ability is to be preserved it can be an interesting approach to protect HLLs from avoidance. In this paper the proposal for a new mining algorithm based on Animal Migration Optimization is made to decrease the number of Association Rules called ARM-AMO. The idea is to remove from the data rules which are not highly supportive and unnecessary. First of all, common item sets and association rules are generated with an Apriori algorithm. AMO also reduces the number of association rules incorporated in a new fitness function. In here, we provide a well-organized mechanism for incident derivation under the unwanted incident. This mechanism very useful for measure the heavy load of an incoming incident and exact calculation of the probability. In additional method is a Select-ability mechanism, which performs an important responsibility in incident derivation under the unwanted incident in both the settled and the unknown incident. A model for signifying derivative incident introduced jointly with an Advanced Sampling Technique that come close to the derived incident probabilities. This augmentation executed the prioritization techniques. In this prioritization techniques, recognize such cases in which the order of incident finding is strong-minded and mechanism for the definition of a settled detection execution.