K-means clustering is a method that groups data into k number of clusters by assigning each data point to the cluster with the closest mean value. A report illustrates that when k-means is applied to a dataset shaped like a lemniscate (an infinity symbol), it successfully divides the data into two clear clusters if k is set to 2. Increasing k to 4 still yields a reasonable outcome, splitting the data into smaller, more defined clusters.
K-medoids clustering is similar to k-means but it uses the most central point of a cluster, known as the medoid, instead of the mean. In the case of the lemniscate data, k-medoids also effectively separates the data into two clusters for k = 2. For k = 4, k-medoids creates clusters around the most central data points, providing a result that’s similar to k-means but focused on the medoids rather than means.
DBSCAN clustering works differently by forming clusters based on areas of high data point density. It is less influenced by outliers compared to k-means and k-medoids. With the lemniscate dataset, DBSCAN identified four clusters, recognizing areas of density that are separated by less dense regions.
Some key observations from the report include:
– K-means and k-medoids will partition data into exactly k clusters, even if the natural number of clusters is different. This makes the choice of k crucial.
– DBSCAN doesn’t require setting the number of clusters beforehand. It can find a varying number of clusters based on the density of the dataset, potentially providing a more natural clustering for certain types of data.
– For data with clear separations, like the lemniscate shape, k-means and k-medoids can be effective if the correct value of k is chosen.
– DBSCAN’s strength lies in its ability to manage noisy data and discover clusters without needing a pre-specified number of clusters. This can be particularly useful for datasets where the number of clusters is not known in advance or is uneven.