A case on A Strategy for Mining Association Rules Continuously in POS Scanner Data I will explain the case, and application for asscoiation rules Association rules is learned prior to lecture So this methodology’s explanation will be skipped and jump to case study. Let’s move on to the next page

1. Point of Scanner Data POS: Point of Sale Each time a transaction is made in a POS (or a basket) By the time new data is generated, operational procedures are made In each transaction recorded by a system for reading barcodes. There is information about product purchased, quantity and price, and there could be one or more products in one transaction.

2. A Strategy for Data Mining in POS Scanner Data A. POS Scanner facts The number of daily transactions can easily reach ten thousand even in small stores. Here, transactions are accumulated in a period of one day, and the daily transactions are analyzed. The nature of POS scanner data introduces many problems whilst working with a Data Mining application. If the data quality problem is not so serious as to affect the extracted results, then the quantity becomes the focus of existing difficulties in Data Mining.

B. Divide and Conquer Strategy In market basket, the whole problem is to find Association Rules in a set of all transaction within a period, a day here. This problem can be divided in time intervals (one day here in this case). Association Rules are discovered in all resulting sets.

3. Case Study: Divide and Conquer strategy in Practice A. Strategy Parameters and Choices 2 supermarkets in Brazil are equipped with scanning systems for reading barcodes and will be identified. The associations were generated on a daily basis, being accumulated in a general rules based. Association rules were induced and analyzed to see if they are or not present in transactions. Support and Confidence . This chapter is Divide and conquer strategy in practice First section is strategy parameters and choices 2 supermarkets in brazil are equipped with canning systems for reading barcodes. The associations are generated on a daily basis and being accumulated in a genernal rules base.. This method produce the Group, support, confiendce. And transanction data.

Association rules are useful, meaningful, and significant when its stability value is over 95% and confidence value is over 45%. The aggregation level was defined to treat products closer to their functions and far from brands and major physical aspects. To limit daily rules, the extraction support parameter was to set a minimum limit of 1%.