The k-means algorithm is widely used in a number applications like speech processing and image compression.
This script implements the algorithm in a simple but general way. It performs four basic steps.
1. Define k arbitrary prototypes from the data samples.
2. Assign each sample to the nearest prototype.
3. Recalculate prototypes as arithmetic means.
4. If a prototype changes, repeat step (2).
Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated.
Hard and soft k-means implemented simply in python (with numpy). Quick and dirty, tested and works on large (10k+ observations, 2-10 features) real-world data.
An implementation of "k-Means Projective Clustering" by P. K. Agarwal and N. H. Mustafa.
This method of clustering is based on finding few subspaces such that each point is close to a subspace.
K-means image segmentation based on histogram to reduce memory usage which is constant for any image size.
This is a tool for K-means clustering. After trying several different ways to program, I got the conclusion that using simple loops to perform distance calculation and comparison is most efficient and accurate because of the JIT acceleration in...
[L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X
into k (or, rarely, fewer) clusters by applying the well known batch
K-means algorithm. Rows of X correspond to points, columns correspond to
KNMCluster is an implementation of the K-means clustering algorithm. It takes inputs k and indata. k is the initial guess of the number of clusters.
indata is the aggregate data that you...
Description DC is simple and effective which can outperform the K-means and AP algorithm.
PBKM is simple and effective which can outperform the K-means algorithm.
This is an implementation of the paper
k-means++: the advantages of careful seeding.
It converges very quickly.
Clustering problems are solved using various techniques such as SOM and K-Means. The generic problem involves multi-attribute sample points, with variable weights. We use Genetic Algorithms to build a scalable, generic & easy to use solution.
Efficient implementation of K-medoids clustering methods. This method is similar to K-means but more robust.
For more detail, please see
Input data are assumed column vectors.
This small tool selects colors for a number of patches so that no any neighboring pair of patches have the same color. The tool was motivated by representing clusters produced by k-means.
Clustering is one of the most important data mining techniques used to extract useful information
from microarray data. Microarray data sets can be either clustered by samples or by genes. In this
research we focus on the gene clustering...
KMEANSK - mex implementation (compile by mex kmeansK.cpp
Also an equivalent MATLAB implementation is present in zip file
Performs K-means clustering given a list of feature vectors and k. The argument k indicates the number of...
Statistics modules in Perl Data Language, with a quick-start guide for non-PDL people. They make the PDL shell work like R, but with PDL threading (fast automatic iteration) of procedures including t-test, linear regression, and k-means clustering.
This function performs kernel version of kmeans algorithm. When the linear kernel (i.e., inner product) is used, the algorithm is equivalent to standard kmeans algorithm.
K: n x n a semi-definite matrix computed by a kernel...
The LTI-Lib is an object oriented library with algorithms and data structures frequently used in image processing and computer vision.
The main goal of the LTI-Lib is to provide an object oriented library in C , which simplifies the...
This function is for training a codebook for vector quantization. The data set is split to two clusters, first, and the mean of each cluster is found (centroids). The disttance of each vector from these centroids is found and each vector is...