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k-means clustering has been used as a feature learning (or dictionary learning) step, in either supervised learning or unsupervised learning. [53] The basic approach is first to train a k-means clustering representation, using the input training data (which need not be labelled).
Supervised learning; Unsupervised learning; Semi-supervised learning ... Cluster analysis or clustering is the task of grouping a set of objects in such a way that ...
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision.
Supervised learning, where the model is trained on labeled data Unsupervised learning , where the model tries to identify patterns in unlabeled data Reinforcement learning , where the model learns to make decisions by receiving rewards or penalties.
The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of () and requires () memory, which makes it too slow for even medium data sets. . However, for some special cases, optimal efficient agglomerative methods (of complexity ()) are known: SLINK [2] for single-linkage and CLINK [3] for complete-linkage clusteri
Central applications of unsupervised machine learning include clustering, dimensionality reduction, [7] and density estimation. [ 51 ] Cluster analysis is the assignment of a set of observations into subsets (called clusters ) so that observations within the same cluster are similar according to one or more predesignated criteria, while ...
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.
Consensus clustering is a method of aggregating (potentially conflicting) results from multiple clustering algorithms.Also called cluster ensembles [1] or aggregation of clustering (or partitions), it refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better ...