Pajek Clustering

REVClusterMap API is a simple implementation of clustering for the iOS platform. An extra layer of functionalities that enables clustering built on top of the MapKit Framework. With a small set of customizable settings the simple cluster algorithm provides an easy way to cluster your annotations....

The High Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifiers for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.Reference: C. Bouveyron, S. Girard and C. Schmid, High-Dimensional Data Clustering,...

Efficient implementation of K-medoids clustering methods. This method is similar to K-means but more robust.For more detail, please seehttp://en.wikipedia.org/wiki/K-medoidsInput data are assumed column vectors.tryload data;label=kmedoids(X,3);scatterd(X,3);


Platforms: Matlab

Several operation (shortest route searching, clustering coefficient, strongly connected components, distribution of edges) is implemented on this network. The module has very resource-consuming parts so in a huge site you have to test the performance.The module includes a graph visualization...


Platforms: Windows, Mac, *nix, PHP, BSD Solaris

The Spherical Self-Organizing Feature Maps is a contemporary technique for data clustering and visualization.The main advantages they offer are the following:1. Smooth training2. Implementation in arbitrary dimension without additional computational cost.3. Data visualization in arbitrary...


Platforms: Matlab

bio library is a collection of algorithms and data structures that are useful when building various bioinformatics-related utilities and tools. Here are some key features of "bio library": ?*A* Sequence data type supporting protein and nucleotide sequences and conversion between them, quality...


Platforms: Mac

OpenRDS (Open Requisition Distribution System) is a framework written in Java/RMI conceived for making requisition-based distributed systems (clustering) easy to develop/manage. It's design emphasizes robustness, simplicity and scalability.


Platforms: Windows, Mac, Linux

Bayesian hierarchical clustering methods provide a powerful tool for phylogenetic analysis, linguistic research and hierarchical clustering in general such as applied in marketing, political science, customer preference grouping etc. Bayesian methods use MCMC sampling which results in a large...


Platforms: Mac

ClusterMX is developed as a CLI-based tool that is able to implement various clustering algorithms. Among those algorithms we can find: · K-Means clustering optimized by random walks; · Weighted K-Means (applying force filed to the multidimensional clustering space); · EM Clustering...


Platforms: Windows

K-tree provides a scalable approach to clustering inspired by the B+-tree and k-means algorithms. Clustering can be used to solve problems in signal processing, machine learning and other contexts.


Platforms: Windows, Mac, Linux

RPyC, or Remote Python Call, is a transparent and symmetrical python library for remote procedure calls, clustering and distributed-computing.


Platforms: Windows, Mac, Linux

The Affinity Propagation (AP) clustering algorithmproposed by Frey and Dueck (2007) provides an understandable,nearly optimal summary of a data set. However,it suffers two major shortcomings: i) the number of clusters isvague with the user-defined parameter called self-confidence,and ii) the...


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

A MATLAB spectral clustering package to handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. We implement various ways of approximating the dense similarity matrix, including nearest neighbors and the Nystrom method.


Platforms: Windows, Mac, BSD

Performs hierarchical clustering of data using specified method andseraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Database (2001)".Namely, it searches cutoff where groups...


Platforms: Matlab

ClusterIt is a collection of clustering tools to allow simple administration of groups of UNIX machines. It can also be used to facilitate parallel shell scripting, and compiling.


Platforms: Mac, BSD, Linux

This project implements the techniques used in this paper: @INPROCEEDINGS{Andres10a, author = {JesAss Andrés-Ferrer and Germán Sanchis-Trilles and Francisco Casacuberta}, title = {Similarity Word-Sequence Kernels for Sentence Clustering}, booktitle = {Proceedings of the 8th...


Platforms: Mac, Linux

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 want to put into clusters.The returned value "means" are the eturned means...


Platforms: Matlab

This function takes clustering data and plots it, if the data is 2d or 3d. The inputs are:An m-by-d matrix of data points, where d is the number of dimension and m the number of data pointsAn m-by-1 vector of indices that associate each data point with a cluster.Optional inputs:A c-by-d matrix...


Platforms: Matlab

Algorithm StrAP, is able to adaptively cluster data streams with Affinity Propagation. Several steps are made to buildStrAP. The first one (Weighted AP) extends AP to weighted items withno loss of generality. The second one (Hierarchical WAP) is concernedwith reducing the quadratic AP complexity,...


Platforms: Matlab