The Coronavirus disease (COVID-19) pandemic has led to a huge loss of human life. It has also severely affected the economic, social, and health systems around the world. Frequent pattern mining is one of the main research topics in data stream mining. It is significant in many critical applications, especially in the medical field. This paper proposes a Compressed Maximal Frequent Pattern based on a Damped Window model over a data stream (CMFP-DW). Its main contribution is to integrate the concept of correlation with the purpose of finding valuable patterns that are highly correlated. As such, a new type of pattern is defined, namely the correlated compressed maximal frequent pattern. The CMFP-DW approach is employed for mining accurate correlated maximal frequent patterns from streaming data, and it has been validated against a real-world COVID-19 dataset from the healthcare domain. Frequent patterns generated from this dataset are exploited with the purpose of detecting the COVID-19 cases in different countries of the world. This helps decision makers take the appropriate precautions to prevent the further spread of the COVID-19 pandemic across the world. The six experiments carried out show that the proposed approach outperforms two other existing approaches, namely the estDec and the CP-Tree algorithms regarding accuracy in extracting correlated maximal frequent patterns, memory usage, and the required response time.
COVID-19, Data stream, Frequent pattern mining, Damped window, Data stream mining, Interestingness measure, Correlation, Bond measure.
Asmaa S. ABDO, Hatem M. ABDUL-KADER, Rashed K. SALEM, "Enhanced Compressed Maximal Frequent Patterns from COVID-19 Streaming Data", Studies in Informatics and Control, ISSN 1220-1766, vol. 31(1), pp. 99-108, 2022. https://doi.org/10.24846/v31i1y202210
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