Glosarium AI
Kamus lengkap Kecerdasan Buatan
K-means Clustering
Iterative partitioning algorithm that groups data into K clusters by minimizing within-cluster variance.
Hierarchical Clustering
Method that builds a hierarchy of clusters using a bottom-up (agglomerative) or top-down (divisive) approach.
DBSCAN
Density-based clustering algorithm that identifies clusters of arbitrary shapes and detects outliers.
Principal Component Analysis (PCA)
Linear dimensionality reduction technique that projects data onto axes of maximum variance.
t-SNE
Non-linear dimensionality reduction algorithm specialized in high-dimensional data visualization.
UMAP
Modern dimensionality reduction technique that better preserves global structure than t-SNE with faster computations.
Spectral Clustering
Method using the eigenvalues of a similarity matrix to perform clustering on non-convex data.
Autoencoders
Unsupervised neural networks that learn a compressed representation of data through encoding-decoding.
Gaussian Mixture Model Clustering
Probabilistic approach modeling data as a mixture of Gaussian distributions for soft clustering.
Matrix Factorization
Dimensionality reduction technique decomposing a matrix into products of lower-rank matrices.
Fuzzy Clustering (Fuzzy C-means)
A clustering variant where each point can belong to multiple clusters with different degrees of membership.
Isomap
A manifold learning algorithm that preserves geodesic distances for dimensionality reduction.
LDA (Latent Dirichlet Allocation)
Probabilistic model for dimensionality reduction and clustering in text analysis and topic modeling.
OPTICS
Extension of DBSCAN producing a clustering order that allows identifying structures with variable densities.
Feature Selection
Dimensionality reduction by selecting the most relevant variables rather than creating new combinations.