## 2 Related Literature Reviews  
Review works on association mining, itemset mining, market basket analysis, and data mining can be found in the literature. This section presents review papers for the mentioned fields, with their respective factors and shortcomings. The task of discovering correlations between elements in a data set is known as association rule mining. [PERSON] et al. [32], in their review, focused on the basic concepts of association mining such as itemset identification, rule creation, and general optimizations. In this review, we organized the significant essential contributions made in association mining research, emphasizing the development of the underlying concepts regarding item identification and rule generation. Market basket analysis is one of the most-used methods in data mining, which is used to examine the purchasing behaviors of customers. It is a method used for determining the links or associations between the various goods in a customer's cart. [PERSON] et al. [33] discussed the various approaches for market basket analysis. Most of their primary studies focused on using the Apriori algorithm for generating frequent itemsets; they did not describe other algorithms for market basket analysis in detail. High-utility pattern mining is a new data science approach that involves finding patterns with high significance in a data set. A pattern's usefulness may be assessed using a variety of objective measures, such as profit, frequency, and weight. [PERSON] et al. [34] presented an overview of high-utility itemset mining. They reviewed the state-of-the-art algorithms, expansions, and applications for high-utility itemset mining; furthermore, they examined the basic strategies for exploring the search space of itemsets used in high-utility itemset mining algorithms and presented the problems associated with high-utility itemset mining. Finally, they proposed several research opportunities, such as enhancing the performance and extending the algorithms to incorporate more complex data patterns, as well as applications in novel fields. Correlated high-utility pattern mining (COHUPM) is an extension of high-utility pattern mining. COHUPM uses both utility and correlation measurements to uncover intriguing high-utility patterns. The COHUPM approaches in the literature were studied and compared by [PERSON] et al. [35], who reviewed the most up-to-date methodologies, critical metrics, data structures, and pruning features in great detail. Their work highlighted the various data structures used in COHUPM techniques and discussed different pruning properties. They also pointed out the necessity of new and efficient data structures and pruning properties for better performance. In addition, they discussed the present difficulties inherent to COHUPM techniques and future research directions. The newest research on incremental high-utility itemset mining algorithms was presented by [PERSON] et al. [36]; it included techniques for storing itemsets and utilities using tree, list, array, and hash set storage structures. There are very few review papers available in the field of biblioming. [PERSON] et al. [37] presented a thorough assessment of the literature as well as a categorization system for data mining techniques used in academic libraries. A total of 41 practical contributions from 1998 to 2014 were selected and evaluated for direct relevance. They categorized each article into four primary data mining functions: classification, regression, association, and clustering. The main goal of the literature review of [PERSON] et al. [38] was to determine the approaches and technologies that are used in academic libraries to analyze big data, and to see how much profit could be made from them. Their study provided basic knowledge regarding the benefits of big data in academic libraries and future research prospects. [PERSON] et al. [39] investigated the concept of big data from the perspective of information technology department staffs at three major university libraries in Jordan.