Advanced Temporal Fuzzy Utility Pattern Analysis for Dynamic Uncertain Data Streams

High temporal fuzzy utility pattern analysis extracts patterns by considering temporal factors at which items appear, as well as expressing patterns in a linguistic way that humans can easily understand based on the fuzzy theory. However, in real world stream data environments, data often involve inherent uncertainty, such as inaccuracies in sensor data. Nevertheless, previous studies utilizing the temporal aspect for pattern analysis and fuzzy set theory do not explicitly account for such uncertainty or inaccuracies. Motivated by the limitation, we introduce a scalable method to extract high temporal fuzzy utility patterns within dynamic uncertain data streams. By incorporating uncertainty associated with items appearing in each transaction, our method extracts uncertainty-based high temporal fuzzy utility patterns that more accurately reflect real world conditions. The pattern expansion process is conducted efficiently with a list structure and various pruning methods. Experimental results indicate the proposed approach exhibits superior performance on runtime and memory usage compared to previous methods under diverse membership functions. Moreover, our method exhibits the highest scalability for increasing data volumes and maintains stable sensitivity to threshold variation while exhibiting the completeness and significance of extracted results. Evaluations under various uncertainty distributions show the superior performance of our approach, and effectiveness evaluations indicate contributions of proposed components. The case study involving the concept drifting evaluation indicates the applicability of our approach in various real-world environments with better performance. The implementation is available at https://github.com/sejongdmlab/ TFUN.