## 5 Affinity Analysis Review
### E-Commerce  
[PERSON] et al. [16] proposed a mechanism for mining association rules for online e-commerce recommendations. For this purpose, they attempted to infer the hidden habits of customers for product recommendations. The proposed framework consists of two major phases. In the first phase, affinity grouping is performed for a large number of records. This phase is called the clustering module. Clustering is performed based on a self-organization map (SOM) and an artificial neural network (ANN). The next phase is to derive association rules in each cluster and find relations between different clusters using a rough set. The ideas of lower and upper sets are included. They divided the clients, goods, and business vendors into clusters by SOM.  
The work by [PERSON] et al. [76] provides a novel bi-directional affinity measure framework. They conducted an experiment on a groceries data set to assess whether the bi-directional measuring approach of frequent patterns could function well in large data volumes. The IBM SPSS Modeler data set (1000 shopping records and 11 items) was also used to test the coherence and consistency of the suggested scheme. The evaluation results indicate that the novel association rule measurement approach provides excellent accuracy and stability for evaluating and determining association rules. It can also be utilized in business settings for paired recommendations.  
The authors of [57] focused on the analysis of market basket data; they found affiliations among items within large groups of transactions. They introduced the weighted confidence (WC) affinity function to assess the resemblance between clusters. This method is non-parametric, as the user does not need to provide any input parameter, and each item is represented using a bit vector to employ the vertical data structures, such that utilizing logical operations to speed up the clustering process is possible. After an evaluation, it was reported that the WC-clustering method can determine the proximity between clusters more effectively than other affinity measures.  
In the paper [47], the authors came up with a solution for grouping classified data such as market basket data sets. They proposed a parallelized hierarchical clustering technique for categorical data. This approach is hierarchical and, hence, non-parametric (i.e., the user does not need to provide input parameters); it was implemented using MPI. This clustering approach represents each item as a bit vector to utilize vertical data structures, and applies logical operations to accelerate the computation of itemset support and the merging process. Experiments were conducted on standard benchmark market basket data sets, and the results demonstrated that the proposed PH-clustering method outperforms the sequential approach drastically without compromising the clustering quality; furthermore, this acceleration has a significant impact in large-scale data sets.  
[PERSON] et al. [67] presented a new extraction technique for cumulative database systems, which may be used to determine the high-utility itemsets that have been uncovered. To find a highly profitable itemset with a significant frequency affinity, in each transaction, affinity utility values were used instead of high-utility values. In addition, a knowledge-weighted utilization (KWU) upper bound was used to preserve the downward closure characteristic. Their experimental results revealed that the recommended utility mining strategy has improved the runtime and memory utilization when compared to other conventional utility mining methods, such as LIHUP and FUP-HAUIMI, in a cumulative database.