How do you use Excel for hierarchical clustering compared to other tools?

Ex, Let's say your company has collected data on the amount of money each customer has spent on different product categories. You want to figure out which customer segments are more profitable. Hierarchical clustering on this dataset can be performed using Excel's built-in Data Analysis Toolpak. Organize the data into a matrix with rows representing each customer and columns representing each product category. Then select the hierarchical clustering option and specify the distance metric and linkage method they want to use for the clustering. Excel's graph capabilities can be used to visualize the clusters, identify any patterns or trends and identify which customer segments are most profitable or have the highest potential for growth.

The scatter plot visual that's already built into Excel is so helpful as a starting point for visualizing hierarchical clustering. Once we scale the fields we want to put on the axes for a two-dimensional chart, we can actually see which data points are close to each other on the scatter plot. We can calculate the distances between them using Excel formulas. Lastly, we can use a hierarchical clustering visual called a dendrogram to create a heat map with a tree showing how close the pairs of data points are (based on the distance calculations). While we can't add the tree structure to a visual directly in Excel, we can use conditional formatting to replicate the color gradient found in the visual that we can create in Python or R.

Size limitations can often work in our favor rather than against us by encouraging us to keep our model size smaller and nimbler. We can calculate hierarchical clustering ourselves manually. While this takes a lot longer than running it through R/Python, it enables us to really understand how the algorithm works. We calculate the distance between all the data points (using the Euclidean distance formula for example) and then pair them up to create what looks like a tree if we were to draw it out. We can create something similar to a dendrogram using conditional formatting to add color gradients to show how close the branches are to each other. It doesn't display the branches we see when we create a dendrogram heat map in R/Python though.

Size limitations: Excel has a maximum number of rows and columns that it can handle. If your data is too large, you may need to use a more powerful tool, such as a database or specialized software. Memory limitations: Excel uses your computer's memory to store data and perform calculations. If you have a lot of data or complex calculations, Excel may slow down or crash. Limited statistical analysis: Excel's built-in statistical functions are basic and may not be sufficient for more advanced analysis. You may need to use specialized statistical software to perform more complex analysis.

Excel is a preferred choice for many due to its intuitive interface, especially for straightforward data tasks. However, when it comes to hierarchical clustering, Excel shows its limitations. It isn't built for intricate data analyses and lacks built-in functions for determining optimal cluster numbers or selecting the best linkage methods. Visualization options in Excel are also restrictive, often making it challenging to depict cluster hierarchies effectively. Additionally, Excel can be slower, error-prone, and hard to automate for such tasks. In contrast, while tools like Python or R have a steeper learning curve, they offer extensive capabilities, adaptability, and are better suited for complex analyses and handling large datasets.

I use R and Python a lot for this! R seems to work better for playing with models and Python is more for larger models and deployment. But it also depends on the existing infrastructure within an organization too!

R provides a rich ecosystem of packages and functions for statistical computing, Python provides flexibility and scalability for data analysis tasks, and SPSS offers an easy-to-use interface (so the interface is similar to Excel) to perform statistical analysis easily. Depending on the specific requirements and preferences, we can choose the tool that best suits our needs for hierarchical clustering and other data analysis tasks.

If you came here looking for the "how", I've added a few links below: - https://www.solver.com/hierarchical-clustering-example - https://www.unistat.com/guide/hierarchical-cluster-analysis/ - https://www.youtube.com/watch?v=6ue9UPYyIfg

Hierarchical clustering, a powerful exploratory data analysis tool, faces challenges: computational complexity, determination of cluster numbers, sensitivity to noise and outliers, high memory requirements, difficulties with high-dimensional data, distance metrics selection, result interpretation and scalability. Despite this, it remains valuable. Balancing algorithmic choices and preprocessing techniques helps extract information from the data.

Example use cases: Image analysis: Hierarchical clustering can be used to group similar images together based on their content. This can be useful in fields such as computer vision and image recognition. Biology: Biologists can use hierarchical clustering to group genes or proteins together based on their similarity. This can help identify which genes or proteins are related to specific diseases or biological processes. Social network analysis: Hierarchical clustering can be used to group people together based on their social network connections. This can help identify different communities within a network and understand how they interact

Hierarchical clustering has applications in biology (gene grouping), market segmentation (customer behaviour), text mining (topic structures), image analysis (pixel clustering), social networks (community identification), anomaly detection, customer management (marketing precision), bioinformatics (DNA clustering), neuroscience (brain mapping), and ecology (species classification).