The Association Rules function is often called "Market Basket Analysis", which is used to find relationships or correlations between sets of items2. Market Basket Analysis is an analysis of customer buying habits by searching for associations and correlations between different items customers place in their shopping carts. This function is most widely used for analyzing data in terms of marketing strategy, catalog design, and business decision-making process.
The type of association rule can be expressed as: "70% of people who buy noodles, juices and sauces will also buy bread". Association rules capture items or events in large data containing transaction data. With technological advances, sales data can be stored in large numbers called "basketball data." Association rules defined in basketball data, used for promotional purposes, catalog design, customer segmentation and target marketing. Traditionally, association rules are used to find business trends by analyzing customer transactions. And can be used effectively in the Web Mining field illustrated as follows: in the Web access log, we find that the association rule: "A and B implies C," has an 80% confidence value, where A, B, and C are web pages that accessible. If a user visits pages A and B, then there is 80% chance he / she will visit page C also in the same session, so page C needs to be given direct links from A or B. This information can be used to link dynamically to page C of page A or B so that users can do direct links to page C. This kind of information is used to link to different product pages dynamically based on customer interaction.
The type of association rule can be expressed as: "70% of people who buy noodles, juices and sauces will also buy bread". Association rules capture items or events in large data containing transaction data. With technological advances, sales data can be stored in large numbers called "basketball data." Association rules defined in basketball data, used for promotional purposes, catalog design, customer segmentation and target marketing. Traditionally, association rules are used to find business trends by analyzing customer transactions. And can be used effectively in the Web Mining field illustrated as follows: in the Web access log, we find that the association rule: "A and B implies C," has an 80% confidence value, where A, B, and C are web pages that accessible. If a user visits pages A and B, then there is 80% chance he / she will visit page C also in the same session, so page C needs to be given direct links from A or B. This information can be used to link dynamically to page C of page A or B so that users can do direct links to page C. This kind of information is used to link to different product pages dynamically based on customer interaction.
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