How Do Outliers Affect Clustering Results?
Outliers can significantly influence clustering results in unsupervised learning. Clustering algorithms, such as K-means, rely on distance metrics to group data points based on their similarity. Outliers, which are data points that differ greatly from other observations, can skew these metrics.
1. Misleading Cluster Centers
In K-means clustering, the algorithm seeks to minimize the variance within clusters. The presence of outliers can lead to the calculation of cluster centers that do not accurately represent the majority of the data, resulting in poorly defined clusters.
2. Increased Cluster Count
Outliers can cause the algorithm to create additional clusters or unnecessary divisions within existing clusters. This not only complicates interpretation but also dilutes the meaningfulness of the clusters formed.
3. Reduced Performance
The integrity of clustering results is compromised when outliers dominate the characterization of clusters. This can lead to lower clustering performance metrics, such as silhouette score or Davies-Bouldin index, making it harder to assess the quality of the clustering.
4. Mitigation Strategies
To handle outliers, practitioners can employ pre-processing techniques, such as outlier detection and removal, and use clustering algorithms robust to outliers, like DBSCAN, which can better accommodate noise within the dataset.