## 8 Discussion
### Answers to the Research Questions  
This section provides the answers to our research questions:  
**RQ1: what are the different types of application domains of affinity analysis? **  
Association rules are now widely employed in various disciplines, such as prediction, tailored recommendations, risk analyses, and others. Initial research focused on the application of association mining in market basket analysis [16; 47; 57]. In our review study, we found various application domains of affinity analysis, as described in Section 5. These domains include social network analysis (Section 5.2), natural language processing (Section 5.3), video analysis (Section 5.4), healthcare (Section 5.5), affinity propagation (Section 5.6), and utilities (Section 5.7). **RQ2: how are affinity measures incorporated to improve itemset mining? **  
The problem of discovering correlations between objects in a data set--known as association (rule) mining--has received much interest, especially after the publication of the Apriori algorithm. Market basket data, which enable corporations to better understand purchasing behavior and, as a result, better target market audiences, were a major driving force behind the initial research. Various affinity measures have been incorporated in association mining for better performance. Furthermore, various approaches have been proposed in the literature to improve the overall operation of association mining [32; 7; 6; 96]. A new combined support value was introduced in [97]; it is used in combination with the Apriori algorithm to create package elements that are within a minimum support value. A novel bidirectional affinity measure framework was proposed in [76] to improve the existing association mining approaches. A novel algorithm for affinity-based association rule mining was proposed in [45]. In [16], the authors introduced a new metric called h-confidence. [PERSON] et al. [47; 57] introduced a new weighted confidence (WC) affinity measure to enumerate the resemblance between clusters while reducing the impact of low-support items. The usual approaches for evaluating association rules, which use support, confidence, and accuracy, have several drawbacks. Improvement, conviction validity, lift, Chi-square analysis, and other new evaluation approaches have been discussed in several studies. **RQ3: what are the key biblioming practices and technologies available for academic libraries? **  
The key biblioming practices we observed in our literature survey are listed below:  
* The term \"biblioming\" was first used by [PERSON] et al. [22] to represent data mining for libraries. They presented biblioming as a combination of bibliometrics, data warehousing, and data mining [21]. * [PERSON] et al. [82] utilized biblioming as an essential tool to uncover useful library information from historical data to aid decision-making; however, a systematic procedure was missing for appropriate knowledge discovery. Biblioming was used in conjunction with other measurement and assessment methods to generate complete reports on library systems, in [28; 78]. * Different types of data mining approaches, such as association, classification, regression, clustering, and neural network methods have been applied for the analysis of library data. An association rule-based method was proposed by [PERSON] et al. [98] to evaluate the usefulness of a library collection. * Decision/classification-tree based approaches were proposed by [PERSON] et al. [82], [PERSON] et al. [22], and [PERSON] et al. [83]. * Clustering-based approaches for biblioming analyses were proposed by [PERSON] et al. [70] and [PERSON] et al. [80]. * Bibliometric analyses for university libraries were performed by [PERSON] et al. [25] and [PERSON] et al. [27]. A thorough review of biblioming can be found in Section 6. A summary of the associated studies, along with their contributions, used data sets, evaluations, and limitations, is presented in Table 4.  
**RQ4: what are the existing techniques and approaches for technology mining?