Visualisation is a very important step in all data science problems. Proper visualisations give insight about data and outputs. Different visualisation methods of association rules can add value in several ways. Visualisations of association rules can also aid in communication and presentation of the results to stakeholders or non-technical audiences. Visualisations can make it easier to convey complex information in an accessible and engaging way. They can also help to highlight key findings or insights and facilitate discussion and decision-making. Furthermore, visualisations can enable the exploration of the data from different perspectives, allowing analysts to uncover new relationships or patterns that may have been missed using traditional statistical methods.
Interpretations for non-technical people: Visualisations can assist in presenting the outcomes of the association rules investigation to non-technical stakeholders in a lucid and comprehensible manner. By using visual aids like bar charts or pie charts, the main discoveries can be presented in a straightforward and easily understandable format, which can aid in making well-informed decisions.
Insight into patterns: Visualisation can help identify patterns and trends in the data that may not be immediately apparent through statistical measures alone. For example, a scatterplot or heatmap can reveal clusters of related items, or a network diagram can show the strength of the connections between different items.
Identification of interesting rules: Visualisation can help identify interesting rules that may be missed in a traditional statistical analysis. For example, a tree diagram can show which items are most frequently associated with one another, while a parallel coordinate plot can highlight the relationships between multiple variables.
Validation of findings: Visualisation can help validate the findings of the association rules analysis by allowing users to explore the data from different angles and test the robustness of the results. For example, a scatterplot matrix can be used to explore the relationships between multiple variables and ensure that the association rules are consistent across different combinations of variables.
Comparison of rules: Visualisations can help compare different sets of association rules to identify similarities and differences between them. This can be especially useful for identifying rules that are consistent across multiple datasets or for comparing the effectiveness of different algorithms.
To sum up, visualisation methods offer a comprehensive approach to understand data and generate insights that can’t be obtained using conventional statistical methods alone. For example, visualisations can reveal hidden patterns or connections in the data that are not readily apparent through statistical measures. They can also provide an efficient way to investigate the data and identify possible anomalies or outliers. Additionally, visualisations can simplify complex information, making it more accessible to a broader audience, especially those without technical expertise. Furthermore, they can help to create engaging data stories that aid in decision-making and stakeholder engagement. Visualisations can also identify data limitations and gaps that may require additional data collection or refining analysis techniques.