## 5 Affinity Analysis Review
### Affinity Propagation
After evaluating the coded data set, the experimental results indicated that this model could perform effectively in modifiable queries along with massive coded data sets, generating rapid results. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Major Contribution** & **Data Set Used** & **Implementation Environment** & **Limitations** \\ \hline [PERSON] et al. [45] & The authors proposed an algorithm for affinity-based association rule mining for discovering quasi-equivalence queries. & Experimented on the Purdue University financial database management system with 22 media objects accessed by 17,222 queries. & Conducted two empirical studies, which showed that their proposed approach performed better than the traditional association rule mining approach in discovering quasi-equivalence relationships. & As the structural similarity link between two media objects cannot be automatically discovered, the query execution speed of this algorithm is still slower. & As the structural similarity link between two media objects cannot be automatically discovered, the query execution speed of this algorithm is still slower. \\ \[PERSON] et al. [56] & To capture the degree of affinity in a pattern and remove cross-support patterns, they created a metric called h-confidence and proposed an algorithm called the hyperclique miner. & They evaluated their proposed algorithm on the _Punub_ data set with a 440 MHz CPU and 128 Mbytes of memory. They performed better than existing algorithms used for identifying patterns at very low support levels. & All tests were conducted on a Sun Ultra 10 workstation running the SunG5.5 operating system with a 440 MHz CPU and 128 Mbytes of memory. They performed better than existing algorithms used for identifying patterns at very low support levels. & In practice, cross-support patterns, which contain elements with varying amounts of support, are loosely related and are not suitable for real-world purposes. \\ \[PERSON] et al. [46, 51] & A new algorithm called weighted interesting pattern mining (WIP) was proposed, in which a new metric–weight-confidence–was created to produce weighted hyperclique patterns of similar weight levels. & Three real data sets were used: _Cumect, Punsb, and Mushroom._ & The WIP algorithm was implemented in C++ and experiments were performed on a Unix platform. The algorithm was compared with WFM [86] and hyperclique miner [56]. & This technique is centered on the support confidence structure. However, it does not take into account the actual preferences of users. \\ \hline [PERSON] The authors proposed an association set [16] rule mining mechanism based on the self-organization map (SOM), a neural network for affinity grouping, and the rough set for the extraction of association rules. & Experimented on purchase records of an e-commerce store containing 1120 products and 35 customer records. They only considered records for one whole day of transactions, comprising 2000 records. & The radius and learning rate of SOM clustering were set to 2.0 and 1.0, respectively. The change rate for both radius and learning rate was set to 0.98. The error rate of the clustering was lower than 0.1. For rule extraction, the minimum confidence was set to 0.2. & This strategy cannot be used for dynamic assessment (i.e., it cannot automatically) and feedback and attributes). One should consider more variables, such as customer and product profiles, purchasing purposes, and so on. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the research on affinity analysis.