An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
作者： Zhiwen Jian , Hiroshi Sakai , Takuya Ohwa , Kao-Yi Shen , Michinori Nakata
DOI: 10.1007/978-3-030-52705-1_7
关键词: Information system 、 SQL 、 Complete information 、 Three way 、 Computer science 、 Python (programming language) 、 Apriori algorithm 、 Software tool 、 Improved performance 、 Data mining
摘要: The NIS-Apriori algorithm, which is extended from the Apriori was proposed for rule generation non-deterministic information systems and implemented in SQL. realized system handles concept of certainty, possibility, three-way decisions. This paper newly focuses on such a characteristic table data sets that there usually fixed decision attribute. Therefore, it enough us to handle itemsets with one attribute, we can see frequent itemset defines implication. We make use these characteristics reduce unnecessary improving performance execution. Some experiments by software tool Python clarify improved performance.
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