## 5 Affinity Analysis Review  
Queries aim to access information from media objects that are structurally similar and that exist in various databases, contributing to a need to explore systemic equivalence links throughout the databases (Table 3). A generic affinity-based association rule mining technique for network databases was proposed in [45] for the discovery of quasi-equivalence relationships. There are two phases in the proposed generalized affinity-based association mining procedure. In the first phase, a refinement and interest constraint, as well as minimum interest thresholds, are iteratively checked. In the second phase, a minimum confidence threshold limit is tested; if an unacceptable scenario occurs, additional conditions may also be applied. In order to measure the frequency of two objects that have been accessed together in a set of queries, a relative affinity value between the two objects is utilized. Here, the query set is known to be a transaction set, as each query may request information from one or more database media objects (similar to how each transaction must contain one or more items). They calculated the confidence, interest, and support of their association rules based on mutual affinity values. Using relative affinity metrics gives more insightful feedback, as they indicate the number of query accesses, but not the number of queries. The proposed methodology has been evaluated on the Purdue University database management system (i.e., a real database system). Their experimental results showed that the proposed generalized approach for affinity-based association rule mining could properly extract a set of media objects that are quasi-equivalent from the database. Their proposed mechanism performs better than the basic association rule mining methodology in discovering pairs of quasi-equivalent media objects. To prune the combinatorial search space, current association-rule mining algorithms frequently rely on support-based pruning approaches. This technique is not very practical for data sets with distorted support ratios, as they appear to produce several spurious trends involving objects from various support levels or may skip potentially interesting low-support patterns. [PERSON] et al. [56] suggested using a hyperclique trend to solve these issues; they used an analytical metric called h-confidence to define clear patterns of affinity. To avoid behaviors involving objects with significantly different support levels, they also introduced the modern idea of the cross-support property. They suggested a new hyperclique miner algorithm that utilizes h-confidence cross-support and anti-monotonic properties to successfully discover hyperclique patterns. Later, hyperclique pattern applications were shown to have good ability to detect strong affinity patterns among low-support objects and cluster hyperclique-based items. Their experimental  
Figure 1: Distributions of the publications according to data sources. findings indicated the efficacy of this approach for identifying patterns even at very low support levels in dense data sets, in which most current algorithms break down. [PERSON] et al. [46; 51] suggested a unique algorithm, named the weighted interesting pattern mining (WIP), which applies a new metric of weight-confidence (w-confidence) to weight affinity patterns associated with mining. WIP aims to utilize weight trust and/or h-confidence in the weighted frequent pattern mining algorithm, based on the approach to pattern development, in order to prune uninteresting patterns. In order to determine weight limits, a weight range is used and an h-confidence score allows for the recognition of strong support affinity patterns. The proposed WIP algorithm balances the two measures of weight and support. The performance was improved by incorporating h-confidence and a modified FP-tree structure without the additional cost of computing. Pumsb, Mushroom, and Connect are the three real data sets used for evaluating the proposed algorithm.