Code Directory
 Visual Basic & VB.NET
New Code
White-label Grocery Delivery App Solution 2.0
Best Classified Script 5.1
Readymade B2B Script 1.3.1
Uber Clone with Safety Measure Addons 2.0
Equity Investing Software 1.3.2
C# QR Code Generator 2020.6.0.0
The .Net PDF Library 2020.7.1
dbExpress driver for MySQL 7.2
dbForge Documenter for Oracle 1.2
dbForge Studio for Oracle 4.2
Excel .Net Library 2020.6
fsMediaLibrary.NET 2019.11
VaxVoIP SIP Server SDK 5.2.0
Database Workbench Pro 5.7.4
dbForge Data Generator for Oracle 2.2
Top Code
dbExpress driver for MySQL 3.00
IcrediBB Bulletin Board System 1.0
dbForge Studio for Oracle 3.10
Database Workbench Pro 5.7.4
Availability Booking Calendar PHP 1.0
ATN Site Builder 3.0
Invoice Manager by PHPJabbers 3.0
dbForge Data Generator for Oracle 2.2
ATN Resume Finder 2.0
PHP Review Script 1.0
ICPennyBid Penny Auction Script 4.0
Aglowsoft SQL Query Tools 8.2
Classified Ad Lister 1.0
Solid File System OS edition 5.1
Deals and Discounts Website Script 1.0.2
Top Rated
phpEnter 5.1.
Single Leg MLM 1.2.1
Azizi search engine script PHP 4.1.10
Paste phpSoftPro 1.4.1
Extreme Injector 3.7
Deals and Discounts Website Script 1.0.2
Solid File System OS edition 5.1
Classified Ad Lister 1.0
Aglowsoft SQL Query Tools 8.2
Invoice Manager by PHPJabbers 3.0
ICPennyBid Penny Auction Script 4.0
PHP Review Script 1.0
ATN Resume Finder 2.0
ATN Site Builder 3.0
Availability Booking Calendar PHP 1.0
Fast K-means 1.0
File ID: 81446

Fast K-means 1.0
Download Fast K-means 1.0http://www.mathworks.comReport Error Link
License: Shareware
File Size: 10.0 KB
Downloads: 136
Submit Rating:
Fast K-means 1.0 Description
Description: [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
variables. The output k-by-p matrix C contains the cluster centroids. The
n-element output column vector L contains the cluster label of each
point. The k-element output column vector D contains the residual cluster
distortions as measured by total squared distance of cluster members from
the centroid.

FKMEANS(X, C0) where C0 is a k-by-p matrix uses the rows of C0 as the
initial centroids instead of choosing them randomly from X.

FKMEANS(X, k, options) allows optional parameter name/value pairs to
be specified. Parameters are:

'weight' - n-by-1 weight vector used to adjust centroid and distortion
calculations. Weights should be positive.
'careful' - binary option that determines whether "careful seeding"
as recommended by Arthur and Vassilvitskii is used when
choosing initial centroids. This option should be used
with care because numerical experiments suggest it may
be counter-productive when the data is noisy.


(1) The careful seeding procedure chooses the first centroid at random
from X, and each successive centroid from the remaining points according
to the categorical distribution with selection probabilities proportional
to the point's minimum squared Euclidean distance from the already chosen
centroids. This tends to spread the points out more evenly, and, if the
data is made of k well separated clusters, is likely to choose an initial
centroid from each cluster. This can speed convergence and reduce the
likelihood of getting a bad solution [1]. However, in experiments where
5% uniformly distributed noise data was added to such naturally clustered
data the results were frequently worse then when centroids were chosen at

(2) If, as is possible, a cluster is empty at the end of an iteration,
then there may be fewer than k clusters returned. In practice this seems
to happen very rarely.

(3) Unlike the Mathworks KMEANS this implementation does not perform a
final, slow, phase of incremental K-means ('onlinephase') that guarantees
convergence to a local minimum.

[1] "k-means++: The Advantages of Careful Seeding", by David Arthur and
Sergei Vassilvitskii, SODA 2007.

License: Shareware

Related: spread, evenly, separated, chosen, euclidean, choose

O/S:BSD, Linux, Solaris, Mac OS X

File Size: 10.0 KB

Downloads: 136

More Similar Code

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...

Usage: [means,c]=KNMCluster(k,indata)

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.

User Review for Fast K-means
- required fields

Please enter text on the image