What’s Clustering?
Imagine you’ve dumped legos, toy cars, and action figures into a giant toy box. Now, you want to organize them. You’d naturally group similar toys together, right? That’s essentially what clustering does for data. It’s like a detective trying to group similar cases together without having any labels to guide them through it
Why K-Means?
There are many clustering methods out there, so why choose K-Means? K-Means is one of the simplest and most popular clustering techniques. It’s like trying to find centers in our candy analogy: the candies closest to a center (in terms of flavour) are grouped together. In K-Means, these centers are called “centroids”. The algorithm tries to find the best centroids such that the distance between the datapoints in a cluster and its centroid is minimised.
Digging into the Data
We dove into the police shootings data with a mission: to see if there were any hidden patterns. Using the K-Means clustering method, we grouped the data based on age, gender, race, and signs of mental illness. The outcome? Three distinct clusters:
- Cluster 0: 1583 incidents
- Cluster 1: 6014 incidents
- Cluster 2: 361 incidents
Interpreting the Clusters
- Cluster 0 (1583 incidents): Without diving deep into the data, it’s hard to give a precise interpretation. However, this could represent incidents involving a particular age group, gender, or race. It might also highlight incidents where signs of mental illness were apparent.
- Cluster 1 (6014 incidents): Being the largest cluster, this might represent the most “common” type of incident based on the features chosen. It could be incidents involving a dominant age group or gender, for instance.
- Cluster 2 (361 incidents): This being the smallest cluster could indicate rare cases or outliers. For example, it might represent incidents involving older age groups or a particular combination of features.
Potential Implications
Understanding these clusters can shed light on potential biases or patterns in police shootings. For instance, if one cluster predominantly represents a specific racial group, it could indicate a bias that needs further investigation. On the other hand, if a cluster shows a high prevalence of signs of mental illness, it could point towards the need for better mental health interventions and training for law enforcement officers.
