When we want to find unusual data points, clustering techniques are really helpful. They are part of a type of learning called unsupervised learning. Let’s look at some common clustering methods and how they help us spot these strange points.
K-means is one of the most popular clustering methods. It divides the data into a set number of groups, called clusters. Each data point joins the group that is closest to it.
To find unusual points, K-means checks how far each point is from the center of its cluster. If a point is too far away—beyond a specific distance—we can think of it as an anomaly.
Example: Imagine you have a list of adult heights. K-means can sort these heights into groups like short, average, and tall. If someone is much taller or shorter than the rest, like 3 standard deviations away from the tallest group, we’d consider that an anomaly.
DBSCAN is great for finding unusual points because it doesn’t need a set number of clusters. It can spot outliers as noise. This method groups points that are close together while marking areas with fewer points as anomalies.
Example: Picture a map with GPS locations of cars. Most cars might be found in busy city areas, while a couple of points in quiet rural spots would be marked as anomalies because they are alone out there.
This method creates a tree-like structure of clusters, giving us a view of how data is arranged. Anomalies can show up as tiny clusters that don’t really fit with the bigger groups.
Example: Let’s say you are looking at how customers buy things. Most customers will follow common buying habits, but some may buy very different items. When we look at the tree from hierarchical clustering, we can see these odd buying habits clearly, pointing to possible anomalies.
Isolation Forest is an interesting way to find anomalies using decision trees. Anomalies are usually easier to isolate than regular observations since there are fewer of them. This method works well even with complex data.
Example: In a dataset of credit card transactions, if someone makes a large purchase in a different country right after buying something locally, this unusual behavior would be picked up quickly as an anomaly.
Choosing the best clustering method for spotting anomalies depends on your data and goals.
By learning about these techniques, you can become better at finding anomalies in many areas, like fraud detection in finance or checking health data.
When we want to find unusual data points, clustering techniques are really helpful. They are part of a type of learning called unsupervised learning. Let’s look at some common clustering methods and how they help us spot these strange points.
K-means is one of the most popular clustering methods. It divides the data into a set number of groups, called clusters. Each data point joins the group that is closest to it.
To find unusual points, K-means checks how far each point is from the center of its cluster. If a point is too far away—beyond a specific distance—we can think of it as an anomaly.
Example: Imagine you have a list of adult heights. K-means can sort these heights into groups like short, average, and tall. If someone is much taller or shorter than the rest, like 3 standard deviations away from the tallest group, we’d consider that an anomaly.
DBSCAN is great for finding unusual points because it doesn’t need a set number of clusters. It can spot outliers as noise. This method groups points that are close together while marking areas with fewer points as anomalies.
Example: Picture a map with GPS locations of cars. Most cars might be found in busy city areas, while a couple of points in quiet rural spots would be marked as anomalies because they are alone out there.
This method creates a tree-like structure of clusters, giving us a view of how data is arranged. Anomalies can show up as tiny clusters that don’t really fit with the bigger groups.
Example: Let’s say you are looking at how customers buy things. Most customers will follow common buying habits, but some may buy very different items. When we look at the tree from hierarchical clustering, we can see these odd buying habits clearly, pointing to possible anomalies.
Isolation Forest is an interesting way to find anomalies using decision trees. Anomalies are usually easier to isolate than regular observations since there are fewer of them. This method works well even with complex data.
Example: In a dataset of credit card transactions, if someone makes a large purchase in a different country right after buying something locally, this unusual behavior would be picked up quickly as an anomaly.
Choosing the best clustering method for spotting anomalies depends on your data and goals.
By learning about these techniques, you can become better at finding anomalies in many areas, like fraud detection in finance or checking health data.