Similarities between K-means clustering and Vector quantization
K-means clustering and Vector quantization have 10 things in common (in Unionpedia): Autoencoder, Centroid, Centroidal Voronoi tessellation, Cluster analysis, Image segmentation, Linde–Buzo–Gray algorithm, Lloyd's algorithm, Self-organizing map, Signal processing, Voronoi diagram.
Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.
Autoencoder and K-means clustering · Autoencoder and Vector quantization ·
Centroid
In mathematics and physics, the centroid or geometric center of a plane figure is the arithmetic mean position of all the points in the shape.
Centroid and K-means clustering · Centroid and Vector quantization ·
Centroidal Voronoi tessellation
In geometry, a centroidal Voronoi tessellation (CVT) is a special type of Voronoi tessellation or Voronoi diagram.
Centroidal Voronoi tessellation and K-means clustering · Centroidal Voronoi tessellation and Vector quantization ·
Cluster analysis
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 sense) to each other than to those in other groups (clusters).
Cluster analysis and K-means clustering · Cluster analysis and Vector quantization ·
Image segmentation
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels).
Image segmentation and K-means clustering · Image segmentation and Vector quantization ·
Linde–Buzo–Gray algorithm
The Linde–Buzo–Gray algorithm (introduced by Yoseph Linde, Andrés Buzo and Robert M. Gray in 1980) is a vector quantization algorithm to derive a good codebook.
K-means clustering and Linde–Buzo–Gray algorithm · Linde–Buzo–Gray algorithm and Vector quantization ·
Lloyd's algorithm
In computer science and electrical engineering, Lloyd's algorithm, also known as Voronoi iteration or relaxation, is an algorithm named after Stuart P. Lloyd for finding evenly spaced sets of points in subsets of Euclidean spaces and partitions of these subsets into well-shaped and uniformly sized convex cells.
K-means clustering and Lloyd's algorithm · Lloyd's algorithm and Vector quantization ·
Self-organizing map
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network (ANN) that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map, and is therefore a method to do dimensionality reduction.
K-means clustering and Self-organizing map · Self-organizing map and Vector quantization ·
Signal processing
Signal processing concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements.
K-means clustering and Signal processing · Signal processing and Vector quantization ·
Voronoi diagram
In mathematics, a Voronoi diagram is a partitioning of a plane into regions based on distance to points in a specific subset of the plane.
K-means clustering and Voronoi diagram · Vector quantization and Voronoi diagram ·
The list above answers the following questions
- What K-means clustering and Vector quantization have in common
- What are the similarities between K-means clustering and Vector quantization
K-means clustering and Vector quantization Comparison
K-means clustering has 112 relations, while Vector quantization has 56. As they have in common 10, the Jaccard index is 5.95% = 10 / (112 + 56).
References
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