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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).
Some of the most common algorithms used in unsupervised learning include: (1) Clustering, (2) Anomaly detection, (3) Approaches for learning latent variable models. Each approach uses several methods as follows: Clustering methods include: hierarchical clustering, [13] k-means, [14] mixture models, model-based clustering, DBSCAN, and OPTICS ...
Another method that modifies the k-means algorithm for automatically choosing the optimal number of clusters is the G-means algorithm. It was developed from the hypothesis that a subset of the data follows a Gaussian distribution. Thus, k is increased until each k-means center's data is Gaussian. This algorithm only requires the standard ...
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters).
Centroid-based Clustering: Unsupervised learning method. Clusters are determined based on data points. [1] Fast Global KMeans: Made to accelerate Global KMeans. [2] Global-K Means: Global K-means is an algorithm that begins with one cluster, and then divides in to multiple clusters based on the number required. [2]
Coates and Ng note that certain variants of k-means behave similarly to sparse coding algorithms. [16] In a comparative evaluation of unsupervised feature learning methods, Coates, Lee and Ng found that k-means clustering with an appropriate transformation outperforms the more recently invented auto-encoders and RBMs on an image classification ...
In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data.
In statistics and data mining, X-means clustering is a variation of k-means clustering that refines cluster assignments by repeatedly attempting subdivision, and keeping the best resulting splits, until a criterion such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC) is reached.