Search
New Code
Vue Injector 3.3
Spectrum Analyzer pro Live 2019
Devart Excel Add-in for HubSpot 2.1
RentALLScript - Airbnb clone 2.2
SuiteCRM Theme Customization 7.11.6
iScripts NetMenus 3.1
iScripts EasyIndex 2.2
iScripts EasySnaps 2.0
Australia MyPost shipping For Magento 2 1.0.0
Australia Post eParcel For Magento 1.1.1
Source Control for SQL Server 2.0
Answers phpSoftPro 3.12
Exlcart 2.0
School College ERP 1.3.2
White-label Grocery Delivery App Solution 2.0
Top Code
phpEnter 5.1.
Quick Maps For Dynamics CRM 3.1
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
Code Listing by Michael Chen

Code 1-9 of 9   






This is an implementation of the paper
k-means++: the advantages of careful seeding.

It converges very quickly.



Efficient implementation of K-medoids clustering methods. This method is similar to K-means but more robust.
For more detail, please see
http://en.wikipedia.org/wiki/K-medoids

Input data are assumed column vectors.



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.

Input
K: n x n a semi-definite matrix computed by a kernel...



This is a function performs maximum likelihood estimation of Gaussian mixture model by using expectation maximization algorithm.

It can work on data of arbitrary dimensions. Several techniques are applied in order to avoid the float...



Transform a inner product matrix or kernel matrix to a square of distance matrix



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 is just a brute force implementation of k nearest neighbor search without using any fancy data structure, such as kd-tree. However it is the fastest knn matlab implementation I can find.

A partial sort mex function is implemented...



Normalized mutual information is often used for evaluating clustering result, information retrieval, feature selection etc. This is a optimized implementation of the function which has no for loops.



high accuracy version of log(sum(exp(x)))