Kmeans

This is a very fast implementation of the original kmeans clustering algorithm without any fancy acceleration technique, such as kd-tree indexing and triangular inequation. (actually the fastest matlab implementation as far as I can tell.)This code is as vectorized as possible. Yet it is very...

a kind of usefull clustering algorithm that is better than kmeans and ward hierarchical clustering algorithms in some data sets

This is an implementation of the paperk-means++: the advantages of careful seeding.It converges very quickly.


Platforms: Matlab

The purpose of the development of this toolbox was to compile a continuously extensible, standard tool, which is useful for any MATLAB user for one's aim. In Chapter 1 of the downloadable related documentation one can find a theoretical introduction containing the theory of the algorithms, the...


Platforms: Matlab

A Wrapper for jMEF Libraryhttp://www.lix.polytechnique.fr/~nielsen/MEF/This library allows you to cluster your data using kmeans with different divergence, including Kullback Leibler, to compute centroids and different geometric algorithms in the space of probability distributions.


Platforms: Matlab

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 MATLAB.The code is very simple and well documented,...


Platforms: Matlab

The zip file includes two functions: kmedia.mat and isodata.mat, this functions do two types of clustering methods. Kmedia function do the k-means algorithm, it have tree inputs (X-vector, Y-vector and number of final clusters) and returns the elements of each cluster and the centre of each...


Platforms: Matlab

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.InputK: n x n a semi-definite matrix computed by a kernel function on all sample pairsm: the number of clusters k (1 x 1) or...


Platforms: Matlab

KMEANSK - mex implementation (compile by mex kmeansK.cppAlso an equivalent MATLAB implementation is present in zip filePerforms K-means clustering given a list of feature vectors and k. The argument k indicates the number of clusters you want the data to be divided into. data_vecs (N*R) is the...


Platforms: Matlab

It is known that there is no sufficient Matlab program about neuro-fuzzy classifiers. Generally, ANFIS is used as classifier. ANFIS is a function approximator program. But, the usage of ANFIS for classifications is unfavorable. For example, there are three classes, and labeled as 1, 2 and 3. The...


Platforms: Matlab

This program illustrates the Fuzzy c-means segmentation of an image.This program converts an input image into two segments using Fuzzy k-means algorithm. The output is stored as "fuzzysegmented.jpg" in the current directory.This program can be generalised to get "n" segments from an image by...


Platforms: Matlab

Cluster validation is an important and necessary step in cluster analysis. This visual cluster validation tool CVAP based on GUI provides important tools and convenient analysis environment for validity evaluation of clustering solutions, estimation of the number of clusters and performance...


Platforms: Matlab

K-means image segmentation based on histogram to reduce memory usage which is constant for any image size.


Platforms: Matlab

[L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix Xinto k (or, rarely, fewer) clusters by applying the well known batchK-means algorithm. Rows of X correspond to points, columns correspond tovariables. The output k-by-p matrix C contains the cluster centroids. Then-element...


Platforms: Matlab

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.http://www.mathworks.com/matlabcentral/fileexchange/19344It can also demonstrate the famous four color map...


Platforms: Matlab

allCode.m contains the sequence of code used during the demonstration.The data files:medflies.mat - the fruitfly dataflicker.mat - the flickering light databreastcancer.mat - the breast cancer mortality datasulfate.mat - the blood concentration data.Fisher's iris data are available in the...


Platforms: Matlab

Measure percentage of Accuracy and the Rand index of clustering resultsThe number of class must equal to the number clusterOutput Acc = Accuracy of clustering results rand_index = Rand's Index, measure an agreement of the clustering results match = 2xk matrix which are the best match of the...


Platforms: Matlab

Description DC is simple and effective which can outperform the K-means and AP algorithm.


Platforms: Matlab

PBKM is simple and effective which can outperform the K-means algorithm.


Platforms: Matlab

Clustering is one of the most important data mining techniques used to extract useful informationfrom microarray data. Microarray data sets can be either clustered by samples or by genes. In thisresearch we focus on the gene clustering problem. The objective of gene clustering is to group genes...


Platforms: Matlab