## 5 Affinity Analysis Review
### Affinity Propagation
\begin{table}
\begin{tabular}{p{56.9 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** \\  & & & **and Evaluation** & \\ \hline [PERSON] & The authors introduced the STRAP algorithm, blending affinity propagation (AP) with a statistical change point identification test to group data sets according to the best exemplars. & Two benchmark data sets. & The recommended method can accurately identify the best exemplars from the flow of these exemplars. & The proposed STRAP algorithms take a long time to compute. The question was raised as to whether or not this performance is compatible with real-time applications. \\ \hline [PERSON] et al. [67] & The authors proposed a unique high-utility pattern (HUP) discovery approach for progressive classification databases by considering a resilient frequency affiliation (FA) and knowledge weight utilization, thus preserving the downward closure principle. & Three data samples— retail, food itemset extraction method is excellent and scalable for mining intriguing HUP data. & Experiments showed that the proposed itemset extraction method is excellent and scalable for mining intriguing HUP data. & The proposed data mining approach mainly focuses on improving the run-time and memory usage; other features, such as output accuracy and data security, were not considered \\ \hline [PERSON] et al. [72] & A unique _Deep Affinity Network_ & This paper considers three publicly available data sets of social interactions for evaluation: the Cocktail Party data set [88], the SALSA data set [89], and the Coffee Break data set [90]. & In comparison with earlier work, this algorithm outperformed in conversational group identification, general group identification, and graph clustering tasks, when evaluated against the existing benchmarks. & The suggested technique ignores the temporal relationship of spatial data recorded by a sensor located anywhere. DANTE scales quadratically with the scene size; this could be a problem when used in a more crowded environment. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \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 and Evaluation** & **Limitations** \\ \hline [PERSON] et al. [57] & The authors proposed an HSIC-based definition approach for learning non-linear relationships between patients, which subsequently identifies and connects related data points. & Synthetic data sets comprising 3D manifold shapes with known lower-dimensional 2D representations: punctured sphere, spine on a hyperboloid, Swiss roll with hole, twin peak with hole. & The presented HSIC-based affinity successfully binds identical data points together and separates different data points to give a more explicit portray of response, compared to the Euclidean distance, and it yields high precision under several dimensionality reduction approaches, such as LE, LLE, and LISA & Although HSIC may be a suitable affiliation indicator, the precision together and separates different data points to give a more explicit portray of response, such as the number of neighbors in the graph, the choice of the classification scheme, and so & The suggested Bi-support and Bi-confidence paradigms outperformed previous models, iterabs, such as the number of definition, and application in practice, with excellent precision and reliability for filtering and choosing frequent patterns. & They did not test for large amounts of data (e.g., 24). The second issue definition, and application in practice, with different relevant domains were inadequate. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cont. The study in [68] presented an affinity model based on frequency, context-sensitive interdisciplinary evaluation, fixed approximation, and lightweight profiling of the implemented frequency.