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Abstract

Administrative procedures in various organizations produce numerous crucial records and data. These records and data are also used in other processes like customer relationship management and accounting operations.It is incredibly challenging to use and extract valuable and meaningful information from these data and records because they are frequently enormous and continuously growing in size and complexity.Data mining is the act of sorting through large data sets to find patterns and relationships that might aid in the data analysis process of resolving business issues. Using data mining techniques, enterprises can forecast future trends and make better business decisions.The Apriori algorithm has been introduced to calculate the association rules between objects; the primary goal of this algorithm is to establish an association rule between various things. The association rule describes how two or more objects are related.We have employed the Apriori property and Apriori Mlxtend algorithms in this study and we applied them on the hospital database; and, by using python coding, the results showed that the performance of Apriori Mlxtend was faster, and it was 0.38622, and the Apriori property algorithm was 0.090909. That means the Apriori Mlxtend was better than the Apriori property algorithm.

Keywords

Apriori Mlxtend, Apriori Property, Association Rule Mining, Hospital Readmission, Machine learning, Performance of Algorithms

Article Type

Supplemental Issue

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