What are the most effective ways to use tree maps in data mining?

1 Benefits of tree maps

Tree maps have several advantages over other types of data visualization, such as bar charts, pie charts, or scatter plots. Tree maps can display a large amount of data in a compact and efficient way, without losing the hierarchical structure of the data. Tree maps can also show multiple dimensions of the data, such as value, category, and ratio, by using size, color, and shape of the rectangles. Tree maps can help you identify patterns, trends, outliers, and anomalies in the data, as well as compare different data sets or segments. Tree maps are especially useful for data mining tasks such as clustering, classification, association, and correlation analysis.

2 Best practices for tree maps

Creating effective tree maps requires following best practices and guidelines. Start by choosing a meaningful and consistent hierarchy that reflects the main question or objective of your data mining project, and the level of detail you want to show. For example, if analyzing sales of different products by region and category, use region as the first level, category as the second level, and product as the third level. Additionally, use appropriate colors and scales for your data that match the type and range of your data, while conveying the right message and emotion. For instance, if wanting to show performance of different products, use a diverging color scheme with green indicating high performance and red indicating low performance. You can also use a logarithmic scale if data has a wide variation or outliers. Labels should indicate the name and value of each rectangle, while legends explain the meaning and scale of colors and shapes. Titles and subtitles can summarize main findings or insights from your data mining project. Lastly, interactive features can make tree maps more engaging and dynamic; for example, zooming, filtering, sorting, drilling down functions to change level of detail or focus of tree maps; tooltips, pop-ups or annotations to provide additional information or context for data.

3 Examples of tree maps

Tree maps can be used in data mining to illustrate a variety of different domains and scenarios. For example, a tree map can show the market share of different smartphone brands in various regions and operating systems, helping you analyze competition and customer preferences. It can also display the distribution of COVID-19 cases and deaths by country and continent, allowing you to monitor the global pandemic situation. Additionally, a tree map can show the breakdown of expenses by category and subcategory for a personal budget, helping you manage your finances and track your spending habits.

4 Tools for creating tree maps

When undertaking data mining projects, there are a variety of tools and software that can help you create tree maps. Excel is a popular spreadsheet application that has a built-in feature for creating static tree maps. Tableau is a powerful data visualization software that can create interactive and dynamic tree maps, along with additional features like filters, tooltips, legends, and dashboards. R is a programming language and environment for statistical computing and graphics that can be used to create customized and flexible tree maps, incorporating different algorithms and methods such as clustering, classification, or correlation analysis.

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?