Performance Optimization Simulation Analysis of Artificial Intelligence Inference Engine Based on Data Mining
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DOI: 10.25236/eeems.2018.010
Corresponding Author
Shaodong Hu
Abstract
With the rapid growth of information on the network, search engine has become a necessary tool for knowledge search and knowledge discovery in order to query the required knowledge. Based on this, this paper applies data mining algorithm to objectify reasoning strategies in artificial intelligence, and proposes a unified reasoning strategy model. The purpose is to improve the application scope and code reuse of reasoning strategy, so as to reduce the complexity of reasoning strategy maintenance. The research results show that the quantitative uncertainty inference algorithm is used in the system, which changes the inaccurate results caused by qualitative reasoning, thus helping users to push data information more efficiently and accurately. The research shows that the automatic learning inference system is realized by using the theory and algorithm design in the fields of artificial intelligence and data mining. At the same time, the algorithm has improved efficiency in the process of clustering compared with the system clustering algorithm, and provides a good foundation for the next step of outlier data mining and classification.
Keywords
Data Mining, Artificial Intelligence Reasoning Engine, Performance Optimization