Abstract
Over the last few years, the growth of data is exponential, leading to colossal amounts of information being produced by computational systems. Meanwhile, the data in real-life applications are usually incomplete and imprecise, which poses big challenges for researchers to obtain exact and valid analytical results with traditional frequent pattern mining methods. Since the potential faults can break the original characteristics of data patterns into multiple small fragments, it is impossible to recover the long true patterns from these fragments. To explore the huge amount of imprecise data by means of frequent pattern mining, we propose a service-oriented model that enables a new way of service provisioning based on users' QoS (quality of service) requirements. The novel model is developed to solve the problem of mining top-k approximate closed patterns in imprecise databases and will be further applied to diagnosis and treatment of potential patients in online medical applications. We test the novel model in an imprecise medical database and the experimental results show that the new model can successfully improve the health services for online customers.
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