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K-means clustering and Vector quantization

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between K-means clustering and Vector quantization

K-means clustering vs. Vector quantization

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. Vector quantization (VQ) is a classical quantization technique from signal processing that allows the modeling of probability density functions by the distribution of prototype vectors.

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 · See more »

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 · See more »

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 · See more »

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).

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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).

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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 · See more »

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 · See more »

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.

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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 · See more »

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.

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The list above answers the following questions

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

This article shows the relationship between K-means clustering and Vector quantization. To access each article from which the information was extracted, please visit:

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