1. K-Means Clustering
Suitability:
Given the numeric nature of attributes like age, longitude, and latitude, K-Means can be applied to cluster based on geolocation or age groups.
Advantages:
- Efficient for large datasets.
- Can quickly identify patterns when the number of clusters is known or can be estimated.
Limitations:
- Assumes clusters to be spherical, which might not be suitable for complex geographic distributions.
- Requires the number of clusters to be specified, which might be challenging without domain knowledge.
2. Hierarchical Clustering
Suitability:
Could be used to build a hierarchical structure of incidents based on similarity in attributes, such as geolocation or threat level.
Advantages:
- Can provide a hierarchy of incidents, offering a graded perspective.
- Doesn’t require a pre-specified number of clusters.
Limitations:
- Computationally expensive for large datasets, which might make it less suitable for this dataset if it’s extensive.
3. DBSCAN
Suitability:
Given the geographic attributes (longitude and latitude), DBSCAN can identify dense regions of incidents, which could be cities or neighborhoods with high shooting incidents, and separate them from sparse regions.
Advantages:
- Can identify clusters of varying shapes and densities, making it suitable for geographic data.
- Doesn’t require the number of clusters to be specified.
Limitations:
- May struggle if the density variation between different cities or neighborhoods is vast.
4. Mean Shift Clustering
Suitability:
For attributes like geolocation, Mean Shift could identify clusters without making any assumptions about their shapes.
Advantages:
- Can detect clusters of any shape, suitable for geospatial clustering.
- No prior knowledge of the number of clusters needed.
Limitations:
- Computationally intensive, which might be a concern for large datasets.
- The bandwidth parameter needs careful tuning.
Conclusion
The choice of clustering method for the fatal police shootings dataset largely depends on the objective. For geospatial patterns, DBSCAN and Mean Shift seem promising due to their ability to handle clusters of varying shapes and densities. K-Means could be a quick way to get insights if the number of clusters is known or can be estimated, while hierarchical clustering could provide a structured breakdown of incidents.
Always remember to preprocess the data, handle missing values, and consider feature scaling or transformation to improve clustering results.
