Negative Association Rule untuk Melihat Pola Keterkaitan Perilaku Konsumen pada Data Transaksi Pembelian

Abstract

Association rule mining is one of the most popular data mining techniques to find associations among items in a set by mining necessary patterns in a large database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items or absent from transactions. The behavior of the customers is studied with reference to buying different products in a shopping store, the discovery of interesting patterns in this collection of data can lead to important marketing and management strategic decisions. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other, in devising marketing strategies. This study used the combinations of minimum support of 10%, 30%, 50% and 70% and minimum confidence 10%, 30%, 50%, and 70%, also minimum interest 0.01, 0.001, 0.0001, and 0.00001. The result shows that the highest value is established by the occurence of rules if customers buy soap then customers will not buy soft drinks and otherwise if a customer buys a soft drink, the customer will not buy soap. Both rules have an interesting value 0.02012.