What is Unsupervised learning?
In unsupervised learning the algorithms are not given any labeled data but instead the goal is to find patterns and relationships that are hidden in the input data.
In unsupervised learning the algorithms are not given any labeled data but instead the goal is to find patterns and relationships that are hidden in the input data.
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
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