Clustering Algorithm 편집하기 (부분)

==Types of Clustering Algorithms==
Several types of clustering algorithms are commonly used, each with unique characteristics suited to different types of data:
*'''Partitioning Clustering''': Divides data into non-overlapping clusters, with each data point belonging to only one cluster.
  - Example: k-Means, which groups data into k clusters by minimizing the distance between data points and their assigned cluster centroids.
*'''Hierarchical Clustering''': Creates a hierarchy of clusters, which can be visualized as a dendrogram. Clusters are formed either through a bottom-up (agglomerative) or top-down (divisive) approach.
  - Example: Agglomerative clustering, where each data point starts as its own cluster and merges iteratively based on similarity.
*'''Density-Based Clustering''': Forms clusters based on data density, useful for identifying arbitrarily shaped clusters and handling noise.
  - Example: DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups data points closely packed together and marks sparse areas as noise.
*'''Model-Based Clustering''': Assumes that data is generated from a mixture of underlying probability distributions, with each cluster representing a different distribution.
  - Example: Gaussian Mixture Model (GMM), which models clusters as a mixture of Gaussian distributions and estimates cluster probabilities.