Clustering Algorithm 편집하기 (부분)

==Common Clustering Algorithms==
Each clustering algorithm has specific use cases, strengths, and limitations:
*'''k-Means Clustering''': A partitioning algorithm that divides data into k clusters by minimizing the distance between data points and cluster centroids. Suitable for well-separated spherical clusters.
*'''Agglomerative Hierarchical Clustering''': A bottom-up approach that builds a hierarchy of clusters by iteratively merging the most similar clusters. Useful for data with a hierarchical structure.
*'''DBSCAN (Density-Based Spatial Clustering of Applications with Noise)''': A density-based algorithm that forms clusters based on dense regions in data, effective for irregularly shaped clusters and noise handling.
*'''Gaussian Mixture Model (GMM)''': A probabilistic approach that represents clusters as Gaussian distributions. GMM can capture clusters of different shapes and is flexible in handling overlapping clusters.
*'''Mean Shift Clustering''': A non-parametric algorithm that identifies clusters by shifting data points toward the mean of nearby points. Effective for data with irregular clusters and variable density.