How do you handle outliers and anomalies in your cluster analysis data?

1 Identify outliers and anomalies

The first step is to identify outliers and anomalies in your data, which can be done using various methods, such as box plots, histograms, scatter plots, or z-scores. Outliers and anomalies can be caused by different factors, such as measurement errors, data entry errors, seasonal variations, or unusual events. You should investigate the source and nature of the outliers and anomalies, and decide whether they are relevant or irrelevant for your clustering objective.

2 Treat outliers and anomalies

The second step in data analysis is to treat outliers and anomalies, which can be done using a variety of strategies depending on the situation. One option is to remove outliers and anomalies if they are clearly erroneous or irrelevant for the purpose of clustering, and if they make up a small portion of the data. Alternatively, outliers and anomalies can be replaced with the mean, median, mode, or another value that is representative of the data distribution. A more advanced option is to transform outliers and anomalies with a method such as logarithmic or square root transformation, which can reduce skewness and scale the data to make it more suitable for clustering.

3 Choose a clustering method

The third step is to choose a clustering method that is appropriate for your data and your clustering goal. Different clustering methods have different assumptions, advantages, and disadvantages, and some are more sensitive or robust to outliers and anomalies than others. For example, hierarchical clustering can be affected by outliers and anomalies, as they can influence the formation and structure of the clusters, while k-means clustering can be more resistant, as it assigns each data point to the nearest cluster center. You should also consider the number and shape of the clusters, the distance and similarity measures, and the validation criteria when choosing a clustering method.

4 Evaluate and compare cluster solutions

The fourth step is to evaluate and compare cluster solutions, which can help you assess the quality and validity of your clustering results, and identify the optimal number and configuration of clusters. There are various methods to evaluate and compare cluster solutions, such as internal, external, and relative measures, which can use different criteria, such as cohesion, separation, silhouette, or entropy. You should also visualize and interpret your cluster solutions, and check how they relate to your business objectives and insights.

5 Test and refine cluster solutions

The fifth step is to test and refine cluster solutions, which can help you improve the performance and reliability of your clustering results, and address any issues or limitations. You can test and refine cluster solutions using different techniques, such as sensitivity analysis, stability analysis, or cross-validation, which can measure how your cluster solutions vary or change under different conditions, such as data sampling, outlier treatment, clustering method, or parameter selection. You should also monitor and update your cluster solutions over time, as your data and business environment may evolve.

6 Apply cluster solutions

The final step is to apply cluster solutions to your merchandise planning decisions and actions, which can help you optimize your product assortment, pricing, promotion, allocation, and replenishment strategies. You can use your cluster solutions to segment your products, customers, or stores based on their characteristics and behaviors, and tailor your merchandise plans accordingly. You can also use your cluster solutions to identify opportunities and challenges, such as new markets, niches, trends, or risks, and adjust your merchandise plans accordingly.

7 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?