## 5 Affinity Analysis Review
### Affinity Propagation  
Affinity propagation has been applied successfully in several fields, such as biometric identification. In contrast, [PERSON] et al. [64] implemented an affinity propagation algorithm for text clustering. Their algorithm (due to its efficient and rapid response) applies to issues where data are not in uninterrupted spaces. It is also applicable to issues where asymmetrical affinities and the matches do not meet the triangle inequality. After evaluation, it was found that the default similarity measurement scheme (Euclidean) is not well-suited for clustering textual data. Several proximity measures were used; it was found that the Kullback-Leibler (KL) divergence is better than the Euclidean distance in this regard. Thus, we can conclude that to obtain better performance in text clustering, the KL divergence and the affinity propagation algorithm should be used jointly. Dataflow clustering recognizes the radical dataflow patterns. The authors of [65] emphasized identifying the most relevant exemplars from the flow of data. Two issues must be considered: how to group by following the best exemplars and how to deal with the producing patterns, which are crucial properties of data streams with dynamic distributions. To meet the first challenge, the affinity propagation (AP) algorithm was employed. Then, change detection was applied to address the second problem. The recommended STRAP algorithm blends the AP to group data sets according to the best exemplar; meanwhile, the clustering model is recreated in case the evaluation identifies a change in the radical data distribution. Experimental results demonstrated that the recommended methods can accurately identify the best examples from the dataflow, and then build clusters according to these examples. [PERSON] [PERSON] et al. [58] addressed the use of affinity analysis (AA) in a qualitative investigation to identify the critical factors for e-service challenges in Malaysia. They also demonstrated how affinity analysis can be employed to explain the relative data of an analytical method. The proximity analysis uses the NEWS matrix to provide a new experience in experimentally interpreting the quality of data; it was clearly shown that a similarity analysis of the flawless output can be used to mathematically evaluate qualitative data. This could help researchers to evaluate and perceive the level of confidence when identifying the root cause of the necessary critical factor. This analysis was applied to data from a pilot study consisting of focus groups and individual interviews. The properties of related data groups regarding the familial faults of power transformers were observed and, in order to deal with these properties, several requirements were specified. To fulfill these requirements, [PERSON] et al. [71] introduced an adaptive repeated itemset mining algorithm (depending on the Apriori algorithm). Using this algorithm, the similarity analysis of familial defects of power transformers is executed in a vast number of records obtained from in-service power transformers. After evaluating a large data set, it is clear that the proposed algorithm can effectively specify the similarities among manufacturers, operating years, the voltage grades of transformers, and their defective parts. [PERSON] et al. [73] considered coded data sets, which are often used as brief representations of real-world procedures. A previously coded data set implies an approach that can only work with a small data set. Therefore, in this study, they introduced an information retrieval-based model that could work with a vast amount of data without facing any complexity. Their experiment used artificial coded data sets and data sets collected from independent domains. Real-world data collected from eminent domains (e.g., peddle businesses and healthcare) were used jointly as well as massive artificial coded data sets. By employing a kernel technique and vector distance functions, the proposed method indicates proficient closest neighbor queries.