K-means is a clustering algorithm that aims to partition a set of data points into a specified number of groups or “clusters.” The process starts by randomly selecting “k” initial points called “centroids.” Every data point is then assigned to the nearest centroid, and based on these assignments, new centroids are recalculated as the average of all points in the cluster. This process of assigning points to the closest centroid and recalculating centroids is repeated until the centroids no longer change significantly. The result is “k” clusters where data points in the same cluster are closer to each other than to points in other clusters. The user needs to specify the number “k” in advance, which represents the number of desired clusters.
DBSCAN is a clustering algorithm that groups data points based on their proximity and density. Instead of requiring the user to specify the number of clusters in advance (like k-means), DBSCAN examines the data to find areas of high density and separates them from sparse regions. It works by defining a neighborhood around each data point, and if enough points are close together (indicating high density), they are considered part of the same cluster. Data points in low-density regions, which don’t belong to any cluster, are treated as noise. This makes DBSCAN especially useful for discovering clusters of varying shapes and sizes, and for handling noisy data.
Potential pitfalls:
DBSCAN:
- Requires selecting density parameters.
- Poor choice can miss clusters or merge separate ones.
- Struggles when clusters have different densities.
- Might classify sparse clusters as noise.
- Performance can degrade in high-dimensional data.
- Distance measures become less meaningful.
- Points close to two clusters might be arbitrarily assigned.
K-means:
- Need to specify number of clusters beforehand.
- Wrong choice can lead to poor clustering results.
- Random initialization can affect the final clusters.
- Might end up in local optima based on initial points.
- Assumes clusters are spherical and roughly of the same size.
- Struggles with elongated or irregularly shaped clusters.
- Sensitive to outliers, which can distort cluster centroids.