In statistics, there are several types of clustering, which can be broadly classified into two categories: hierarchical clustering and partitioning clustering.
- Hierarchical clustering: Hierarchical clustering is a method of clustering that involves building a hierarchy of clusters, also known as a dendrogram. An example of this is the hierarchy of species in the animal kingdom. There are two types of hierarchical clustering:
- Agglomerative clustering: Agglomerative clustering is a bottom-up approach, where each data point is initially considered as a separate cluster, and then clusters are merged iteratively based on some similarity metric, until all the data points belong to a single cluster. This process results in a dendrogram that shows the hierarchy of clusters and the order in which they were merged.
- Divisive clustering: Divisive clustering is a top-down approach, where all data points are initially considered as a single cluster, and then clusters are split iteratively based on some dissimilarity metric, until each data point belongs to a separate cluster. This process results in a dendrogram that shows the hierarchy of clusters and the order in which they were split.
- Partitioning (Flat) clustering: Partitioning clustering is a method of clustering that involves dividing the data into non-overlapping partitions or clusters. There are several types of partitioning clustering:
- K-means clustering: K-means clustering is a popular partitioning clustering algorithm that involves partitioning the data into k clusters, where k is a user-defined parameter. The algorithm iteratively assigns each data point to the nearest centroid, and then updates the centroids based on the mean of the data points in each cluster, until convergence.
- Fuzzy clustering: Fuzzy clustering is a method of partitioning clustering that allows a data point to belong to multiple clusters with different degrees of membership. The degree of membership is represented by a fuzzy membership function, which assigns a weight to each cluster based on the similarity between the data point and the centroid of the cluster.
- Density-based clustering: Density-based clustering is a method of partitioning clustering that identifies regions of high density in the data space and assigns them to clusters. The algorithm starts with a seed point and iteratively expands the cluster by adding data points that are within a certain radius or density threshold.