K-means Clustering:
Using the K-means clustering technique, data is grouped into k clusters by allocating each data point to the cluster whose mean value is the closest. A report shows that if k is set to 2, k-means effectively divides the data into two distinct clusters when applied to a dataset shaped like a lemniscate, or an infinity symbol. Splitting the data into smaller pieces still produces a reasonable result when k is increased to 4.
K-Medoids:
Similar to k-means clustering, k-medoids clustering employs the medoid—the cluster’s most central point—instead of the mean. Additionally, for k = 2, k-medoids successfully divide the lemniscate data into two clusters. Similar to k-means but with an emphasis on medoids rather than means, k-medoids for k = 4 generates clusters around the most central data points.
DBSCAN:(Density-Based Spatial Clustering of Applications with Noise)
In contrast, DBSCAN clustering creates clusters around regions with high densities of data points. It is less susceptible to the effects of outliers than k-means and k-medoids. DBSCAN detected four clusters using the lemniscate dataset, identifying dense regions divided by less dense areas.