Research on Fuzzy Temporal Event Association Mining Model and Algorithm

• Published in: Axioms Vol. 12; no. 2; p. 117
• Main Authors: Zhu, Aihua, Meng, Zhiqing, Shen, Rui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Algorithms; Anomalies; Data mining; Data transmission; Feature extraction; Fuzzy algorithms; Fuzzy logic; Fuzzy sets; Fuzzy systems; fuzzy temporal association rules; fuzzy temporal data mining; fuzzy temporal event; Granulation; Mathematical research; Medical research; Securities markets; temporal type; Time series; China
• ISSN: 2075-1680; 2075-1680
• DOI: 10.3390/axioms12020117

As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.