Affinity Analysis Task
An Affinity Analysis Task is a data mining task that discovers correlations between different entities according to their co-occurrence in a dataset.
- Example(s):
- Counter-Example(s):
- See: Discounts and Allowances, Unique Identifier, Cross-Selling, up-Selling, Sales Promotion.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Affinity_analysis Retrieved:2021-12-3.
- Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. In almost all systems and processes, the application of affinity analysis can extract significant knowledge about the unexpected trends. In fact, affinity analysis takes advantages of studying attributes that go together which helps uncover the hidden pattens in a big data through generating association rules. Association rules mining procedure is two-fold: first, it finds all frequent attributes in a data set and, then generates association rules satisfying some predefined criteria, support and confidence, to identify the most important relationships in the frequent itemset. The first step in the process is to count the co-occurrence of attributes in the data set. Next, a subset is created called the frequent itemset. The association rules mining takes the form of if a condition or feature (A) is present then another condition or feature (B) exists. The first condition or feature (A) is called antecedent and the latter (B) is known as consequent. This process is repeated until no additional frequent itemsets are found. There are two important metrics for performing the association rules mining technique: support and confidence. Also, a priori algorithm is used to reduce the search space for the problem. Larose, Daniel T.; Larose, Chantal D. (2014-06-23). Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/9781118874059. ISBN 978-1-118-87405-9.</ref>
The support metric in the association rule learning algorithm is defined as the frequency of the antecedent or consequent appearing together in a data set. Moreover, confidence is expressed as the reliability of the association rules determined by the ratio of the data records containing both A and B. The minimum threshold for support and confidence are inputs to the model. Considering all the above-mentioned definitions, affinity analysis can develop rules that will predict the occurrence of an event based on the occurrence of other events. This data mining method has been explored in different fields including disease diagnosis, market basket analysis, retail industry, higher education, and financial analysis. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers. This information can then be used for purposes of cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans.[1]
- Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. In almost all systems and processes, the application of affinity analysis can extract significant knowledge about the unexpected trends. In fact, affinity analysis takes advantages of studying attributes that go together which helps uncover the hidden pattens in a big data through generating association rules. Association rules mining procedure is two-fold: first, it finds all frequent attributes in a data set and, then generates association rules satisfying some predefined criteria, support and confidence, to identify the most important relationships in the frequent itemset. The first step in the process is to count the co-occurrence of attributes in the data set. Next, a subset is created called the frequent itemset. The association rules mining takes the form of if a condition or feature (A) is present then another condition or feature (B) exists. The first condition or feature (A) is called antecedent and the latter (B) is known as consequent. This process is repeated until no additional frequent itemsets are found. There are two important metrics for performing the association rules mining technique: support and confidence. Also, a priori algorithm is used to reduce the search space for the problem. Larose, Daniel T.; Larose, Chantal D. (2014-06-23). Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/9781118874059. ISBN 978-1-118-87405-9.</ref>
- ↑ "Demystifying Market Basket Analysis". Retrieved 28 December 2018.