Recently, the capabilities of generating and collecting data have been increasing rapidly. Widespread use of bar codes for most commercial products, the computerization of many business, and the advance in data collection tools have provided us with huge amount of retail data. This explosive growth in data and databases has generated an urgent need for data mining techniques and tools that can extract implicit, previously unknown and potentially useful information from data in data storages. One of the most popular data mining approaches is "association rules", which is commonly applied to analyze market baskets to help managers to determine which items are frequently purchased together by customers. Affinity analysis is a data analysis and data mining technique that discovers co-occurrence relationships among activities performed by (or recorded about) specific individuals or groups. In general, this can be applied to any process where agents can be uniquely identified and information about their activities can be recorded. 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.
Eddla, Padmalatha; Reddy, R.Ravinder; and Ramasani, Mamatha
"Selection of Optimal Discount of Retail Assortments with Data Mining Approach,"
International Journal of Computer Science and Informatics: Vol. 1:
3, Article 6.
Available at: https://www.interscience.in/ijcsi/vol1/iss3/6