What is Unsupervised learning?
Identification of hidden patterns and associations from data is unsupervised learning.
Identification of hidden patterns and associations from data is unsupervised learning.
Clustering is used to partition data set into N distinct groups/clusters. These groups are semantically coherent in nature
Exclusive Clustering, Probabilistic (Fuzzy) Clustering, Hierarchical Clustering, Model-based Clustering
Dimensionality reduction is the process of transforming a high-dimensional data set, into a more compact representation with fewer features.
Principal Component Analysis (PCA) is a dimension reduction technique.
Each observation is assigned to one and only one cluster.
Each observation is assigned to one or more clusters with a probability of belonging to each.
K-Means starts by selecting initial centroids for the k-clusters by randomly choosing k observations
This approach starts with all observations either belonging to their own cluster or all observations belonging to one large cluster
A Gaussian Mixture Model describes an underlying distribution that is composed of multiple individual Gaussian distributions
Find out all the ways
that you can