## 5 Affinity Analysis Review
### Affinity Propagation
\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** \\  & & & & **and Evaluation** & \\ \hline [PERSON] et al. [50] & The authors proposed a method to detect hidden affinity associations between individuals on the web. & Used four online discussion data sets: 1) WhatsApp group data (WGD), 2) R community data on Twitter; 3) FreeGodemCam Gitter argument corpus. & HAR-search models only positive than AffinityFinder and could notice every detail to observe the context thoroughly. & HAR-search models only positive interaction sequence(s) (PIS) based on the text context in the conversation history. \\ \hline [PERSON] et al. [60] & The authors introduced an algorithm to analyze the context-free grammar affinity using test sentences originating automatically and randomly. & The set of all sentences that could be obtained from grammar. & After the evaluation, it was shown that the recommended trial and error-based algorithm could effectively analyze context-free grammar affinity and produce the desired output. & This algorithm is not fully capable of being applied to practical systems as its computational cost is high. \\ \hline [PERSON] et al. [73] & The authors introduced an information retrieval-based model that employs kernel methods and vector distance functions for systematic analysis of coded data sets. & Artificial coded data sets to which some renowned domains were added, namely healthcare and market basket analyses. & This model can perform smoothly for modifiable queries with vast amounts of coded data sets, generating rapid results. & The proposed information retrieval model mainly focuses on achieving a high performance, rather than accuracy; hence, it does not deliver high accuracy. \\ \hline [PERSON] et al. [68] & The authors proposed a frequency-based similarity analysis group arrays. An interdisciplinary dataset analysis strategy was also utilized to reconstruct this array based on a access frequency. & Used 11 benchmarks: eight from SPEC CPU2000 and three from the distance-based affinity analysis groups by [PERSON] et al. [87]. & After the evaluation, the experimental results showed that the authentic compiler-based array rearrangement improved most of the program’s performance compared to the references, and complex dataflow with aliases. \\ \hline [PERSON] et al. [64] & The authors introduced a framework named the associative affinity factor analysis (AAFA) for position analysis and spotting; it recognizes actual positions within inconsistent affinities. & The 2016 U.S. presidential election campaign tweets were used as an experimental data set. & Their experimental results showed that the correctness of the suggested framework is very high when compared to various existing modern methods. & Other related information in tweets, such as resources, retweets, taken into account for enhanced categorization. \\ \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** & **Limitations** \\ \hline [PERSON] et al. [58] & Conducted a pilot study to determine the most relevant critical factor that led to dissatisfaction among the users in providing e-services. & A pilot study: 3 groups of 8 people were formed, for a total of 24 participants comprising residents from 13 states in Malaysia. & By using the affinity analysis, they determined the critical factors of qualitative data. According to the findings of the affinity analysis, 80\% of the problem was caused by 20\% of the overall functionality of the serving system. & This paper was a pilot study that introduced only 24 participants; studying with fewer data is more analysis, 80\% of the problem was caused by