Data Analysis of Nobel Prizes in Science Using Temporal Soft Sets

Abstract

AbstractIn many real-life applications, some item sets are only frequent during certain periods of time. However, traditional association analysis techniques may fail to find interesting connections between item sets in this scenario, owing to the ignorance of temporal information. To overcome this difficulty, we try to enhance classical rule extraction by means of temporal soft sets in this study. The concept of temporal granulation mappings is used to produce the granular structure associated with a given set consisting of temporal transactions. By virtue of temporal granulation mappings, temporal soft sets and Q-clip soft sets are defined. On this basis, we construct a temporal soft set based framework for describing and mining temporal association rules. We also develop a temporal association rules mining algorithm, which extends the Apriori method in the temporal soft set setting. Moreover, a case study regarding Nobel Prizes in science is conducted on a real-life data set to validate the efficacy of the presented approach. Experimental results verify the efficacy of the proposed method, and comparative analysis further reveals that our approach can extract some strong and promotive rules that are ignored by traditional methods.KeywordsData miningTemporal soft setQ-clip soft setAssociation ruleFuzzy set