What's your strategy for outliers in data mining and clustering?

1 Identify outliers

The first step is to identify outliers in your data set. You can use various methods, such as box plots, scatter plots, z-scores, or interquartile ranges, to visually or numerically detect outliers. You can also apply domain knowledge and common sense to identify outliers that are unrealistic or inconsistent with the data context. For example, if you are analyzing customer age data, you might want to exclude values that are negative or above 100.

2 Remove outliers

The simplest and most common strategy for outliers is to remove them from the data set. This can reduce the noise and improve the performance of data mining and clustering algorithms. However, you should be careful not to lose valuable information or introduce bias by removing outliers. You should also document the criteria and process of removing outliers, and check the impact of the removal on the results and conclusions of your analysis.

3 Replace outliers

Another strategy for outliers is to replace them with more reasonable values. This can preserve the size and structure of the data set, and avoid losing information or introducing bias. You can use various methods to replace outliers, such as mean, median, mode, or nearest neighbor values. You can also use regression or interpolation techniques to estimate missing or erroneous values. However, you should be aware of the assumptions and limitations of the replacement methods, and validate the accuracy and consistency of the replaced values.

4 Transform outliers

A third strategy for outliers is to transform them using mathematical or statistical functions. This can reduce the impact of outliers on the data distribution and the performance of data mining and clustering algorithms. You can use various methods to transform outliers, such as log, square root, or power transformations. You can also use standardization or normalization techniques to scale the data to a common range or unit. However, you should be careful not to distort the meaning or interpretation of the data by transforming outliers.

5 Cluster outliers

A fourth strategy for outliers is to cluster them into a separate group or category. This can acknowledge the existence and importance of outliers, and explore their characteristics and relationships with other data points. You can use various methods to cluster outliers, such as density-based, distance-based, or model-based clustering algorithms. You can also use anomaly detection or novelty detection techniques to identify and label outliers as abnormal or unusual data points. However, you should be aware of the challenges and trade-offs of clustering outliers, such as choosing the appropriate algorithm, parameters, and evaluation metrics.

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?