What is it about?
In security system, high utility pattern mining of a large number of users mobile trajectories is helpful to analyze user behavior patterns, and enhance the internal prevention of the security system. Currently, the frequent pattern mining for mobile trajectory in security systems do not take into account the differences in the safety levels between staying points and the frequency of their occurrence. Existing high utility mobile trajectory pattern mining methods are unable to discover staying points that meet specific user requirements. To solve these problems, a constraint-based high utility mobile trajectory mining algorithm is proposed (names as HUIM-ILC-ACO). It takes into account the user’s stay time, stay frequency, and the safety level of each stopping point and calculates the utility value of each staying point by incorporating these factors through weighted computation to construct a method for mining high utility mobile trajectory patterns that better align with user interests.