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Relevance of this work is determined by the fact that despite the widespread use of keyword sets as the most common approach for collecting thematic information, there are few publications dedicated to the study of frequent term sets in bibliometric research. Usually, pairs of terms co-occurrence are used to construct the network, as in VOSviewer. Research objective. 1. Testing the impact of adjusting the construction of the IEEE term co-occurrence graph by increasing the significance of "strong links," which often form sets of multiple terms. 2. Identify IEEE Terms describing a relevant topic more commonly encountered in newer publications. Materials and methods. The study used 7,114 bibliometric records from IEEE Xplore for the years 2021-2025, collected based on the query: "IEEE Terms": Artificial Intelligence. Mapping of IEEE terms was performed using VOSviewer, and the FP Growth algorithm was used to identify frequently occurring sets. Results and conclusions. Even the simplest enhancement of the significance of terms forming frequently occurring sets showed that the dominant term "artificial intelligence" moved from a cluster with more general words to a cluster with more theme-related terms. An additional result of the research was the identification of a growing interest in the topic described by the terms: artificial intelligence, training, accuracy, data mining, adaptation models, transformers and vectors, which seems to be a clear and consistent topic. Future research. The author believes that the terms forming frequently occurring sets are important for explaining research topics. Therefore, it is advisable to study the same bibliometric data, but using hypergraphs to represent sets of co-occurring terms.