Book voting analysis of Apriori algorithm

Digital systems record the books we read, items we purchase, or the data of the products we enjoy within their own framework for the purpose of information extraction. The information to be derived from these data through data mining methods is of great importance for digital systems. Data mining aims to speed up the decisionmaking process by uncovering hidden relationships and information in data via mathematical and statistical techniques. In this context, data mining techniques were applied to the dataset containing book votes through use of the Apriori algorithm in this study. In this way, the relationships within the data set were identified, rules were generated, and the connections between the generated rules and user preferences were disclosed. With the application of the Apriori algorithm, rules were produced that show books that were liked together with other books and that authors that were liked together with other authors. As a result of the study, rules were developed regarding which other books a person who liked a particular book and which other authors a person who liked a particular author may also enjoy. The rules produced in the study are expected to be used in book recommendation lists and sales strategies.