Pajek Clustering

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.

MicroArray Genome Imaging and Clustering Tool (MAGIC tool) is a platform-independant java program for analyzing MicroArray data (.tiff scans & .txt godlists) via graphs and clustering operations (including QT-clustering). http://www.bio.davidson.edu/magic

ASK:Webapps front-end Application Server, offers a Java Server Component Model, Clustering & Applications Fault-Tolerance Execution. SHORB:Simple Object Request Broker over HTTP. DBPCS: ddbb Connections Pool, supports Business DB Objects Pooling


Platforms: Windows, Mac, Linux

PanOCT, Pan-genome Ortholog Clustering Tool, is a program written in PERL for pan-genomic analysis of closely related prokaryotic species or strains. Unlike traditional graph-based ortholog detection programs, it uses micro synteny or conserved gene neighborhood (CGN) in addition to homology to...


Platforms: 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

Scarlet is an extension for Atlassian JIRA, the leading Enterprise issue tracking and management software, allowing easy clustering of JIRA application instances, ensuring both high availability and linear scalability.


Platforms: Windows, Mac, Linux

For interactive clustering and visualization, Cluster and TreeView by Mike Eisen are great. XCluster by Gavin Sherlock solves the problem of clustering large numbers of files. Now slcview allows you to script generation of cluster and tree images.


Platforms: Windows, Mac, Linux

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

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

Exercises and case studies for a rigorous approach to risk- and portfolio-management. This booklet stems from the review sessions of the six-day ARPM bootcamp.Contents include:Advanced multivariate statistics; copula-marginal decompositionAnnualization/projection (FFT, cumulants,...


Platforms: Matlab

FCMTHRESH Thresholding by 3-class fuzzy c-means clustering [bw,level]=fcmthresh(IM,sw) outputs the binary image bw and threshold level of image IM using a 3-class fuzzy c-means clustering. It often works better than Otsu's methold which outputs larger or smaller threshold on fluorescence images....


Platforms: Matlab

The FindCore Toolbox (v. 1.0b)David Snyder and Gaetano Montelione, (C) 2010This Matlab Toolbox implements the FindCore algorithm described in Snyder, D.A.; Montelione, G.T. PROTEINS: Struct. Funct. Bioinformaticsm, 2005, 59: 673-86. Clustering algorithms for identifying core atom sets and for...


Platforms: Matlab

This user interface allows a user to load some Affymetrix data in an Excel file and perform some basic operations on it - log transform, log ratio, normalization, noise reduction, filtering, PCA and clustering, in addition to some visualizations like profile plots, boxplots and matrix...


Platforms: Matlab

Clusters data using the Mean Shift Algorithm. testMeanShift shows an example in 2-D. Set plotFlag to true to visualize iterations.


Platforms: Matlab

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

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

Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated.


Platforms: Matlab

The geometric double-entity model method (GDEM) is proposed to gives far-near degrees between clusters (namely, GE distances), which integrate the absolute distance between nearest sample sets and the dense degrees of border regions of two clusters. GE distances are measured based on the...


Platforms: Matlab

(SE) is developed to estimate the number of clusters (NC) for PAM clustering algorithm. By inspecting whether it is stable that two potential clusters are separated or merged, SE focuses on the separability of two closest clusters among k potential clusters (called twin-clusters) when a data set...


Platforms: Matlab

This function performs SAHN clustering (like linkage.m in the Statistics Toolbox) but allows users to define their own inter-cluster distance function. This function is thus more flexible than linkage.m (albeit a lot slower).(C) 2010 by David A. Snyder. Support for writing this code provided by...


Platforms: Matlab