How can you communicate association rule mining results with data visualization?

1 Rule Metrics

Before you create any data visualization, you need to understand the metrics that measure the quality and significance of association rules. The most common metrics are support, confidence, and lift. Support is the proportion of transactions in the dataset that contain the rule's antecedent and consequent. Confidence is the proportion of transactions that contain the rule's antecedent that also contain the rule's consequent. Lift is the ratio of the rule's confidence to the expected confidence if the antecedent and consequent were independent. These metrics can help you filter, rank, and compare association rules.

2 Scatter Plot

A scatter plot is a simple and effective way to visualize association rules using two metrics as the x and y axes. For example, you can plot the support and confidence of each rule as points on a scatter plot, and use the size or color of the points to represent the lift or another metric. A scatter plot can help you identify the rules that have high or low values for both metrics, as well as the trade-off or correlation between them. You can also use interactive features such as tooltips, zooming, or filtering to explore the details of each rule.

3 Matrix Plot

A matrix plot is a type of heat map that shows the values of a metric for all possible combinations of items or categories in a dataset. For example, you can create a matrix plot that shows the lift of each pair of items in a supermarket dataset, where the rows and columns represent the items, and the cells represent the lift values. A matrix plot can help you discover the items that have strong or weak associations with each other, as well as the clusters or patterns of items that often co-occur. You can also use color scales, annotations, or sorting to enhance the readability and meaning of the matrix plot.

4 Network Graph

A network graph is a type of graph that shows the relationships between entities as nodes and edges. For example, you can create a network graph that shows the association rules as nodes and edges, where the nodes represent the items, and the edges represent the rules. The size, color, or shape of the nodes and edges can indicate the values of different metrics, such as support, confidence, or lift. A network graph can help you visualize the structure and complexity of the association rules, as well as the centrality or importance of each item or rule. You can also use layout algorithms, labels, or legends to organize and explain the network graph.

5 Rule Diagram

A rule diagram is a type of diagram that shows the logic and hierarchy of association rules. For example, you can create a rule diagram that shows the antecedents and consequents of each rule as boxes and arrows, where the boxes represent the items, and the arrows represent the rules. The width, color, or style of the arrows can reflect the values of different metrics, such as support, confidence, or lift. A rule diagram can help you illustrate the flow and direction of the association rules, as well as the subsets or supersets of each rule. You can also use indentation, grouping, or numbering to structure and label the rule diagram.

Data visualization is a powerful tool to communicate association rule mining results with your audience. By using different types of data visualization, you can highlight the most important and interesting aspects of the association rules, such as their quality, significance, relationships, logic, or hierarchy. You can also use data visualization to make your association rule mining results more accessible, understandable, and engaging for your audience.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?