K-values:
By reducing the squared distances to the cluster centres, data is divided into K clusters.
assumes that the clusters are uniformly sized and spherical.
needs the number of clusters (K) to be specified in advance.
Unusual cluster forms and noisy data are not well suited.
sensitive to the original location of the cluster centre.
Effective for datasets of a moderate size.
DBSCAN –
identifies clusters in high-density areas and outliers in low-density areas by classifying data points according to their density.
able to locate clusters of any size and any shape.
determines the number of clusters automatically.
robust for managing outliers and noisy data.
less dependent on the parameter selection.
less effective with data that has many dimensions.