Pajek Clustering

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...

The searching process is necessary for the Affinity propagation clustering (AP) when one demands a clustering solution under given number of clusters. The Fast AP uses multi-grid searching to reduce the calling times of AP, and improves the upper bound of preference parameter to reduce the...

Affinity propagation clustering (AP) is a clustering algorithm proposed in "Brendan J. Frey and DelbertDueck. Clustering by Passing Messages Between Data Points. Science 315, 972 (2007)". It has some advantages: speed, general applicability, and suitable for large number of clusters....


Platforms: Matlab

A new soft clustering algorithm is presented (Clustering through Optimal Bayesian Classification). The algorithm does not depend on random initializations, and it contains a native metric to select the optimal number of clusters.The clustering algorithm minimizes the log-Bayesian risk...


Platforms: Matlab

Writes a Pajek ".net" files from a MATLAB matrix, We can set the threshold to extract the useful information from the connection 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 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

In this work we present a generalization of partitional clustering aiming at the inclusion into the clustering process of both distance and direction of the point of observation towards the dataset. This is done by incorporating a new term in the objective function, accounting for the distance...


Platforms: Matlab

This toolbox extends the FindCore toolbox by first clustering the input set of structural models (create_multiensemble) and then superimposing each cluster separately. This allows for appropriate assessment of the uncertainty or flexibility in protein structure when more than one inherent...


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

Computes the clusters of Pixels based upon their color.Computes the clusters of Pixels based upon their color. The algorithm is based uopon binary tree quantization technique described by Orchard and Bouman. The code can be used for generating Mixed Gaussian Models used in graph cut based...


Platforms: Matlab

A toolkit for Gaussian mixtures.flexible tools for:Generating univariate, multivariate, or mixtures of gaussiansInteractive viewing tools allows viewing of multidimensional data and models. Initialize models, add and remove dimensions or clusters and inspect the fit in real-time.Also includes...


Platforms: Matlab

Findexer is a FTP Indexer used to list FTP-servers contents and store listing in database (PostgreSQL). Later, user can query database through web-interface to search filenames by pattern.Findexer might be useful in local area networks with multiple ftp resources.Its core written in C , though...


Platforms: PHP

BitMagic is C++ library designed and developed to implement efficient platform independent bitsets. Key features: * Several types of on the fly adaptive compression. * Dynamic range of addressable space of 232-1 bits. * Efficient memory management. * Cache optimized bitset distance...


Platforms: Mac

Learn how simple rule-driven dynamical systems can produce complex dynamical behavior, like 2nd order phase transitions, criticality and clustering. This simulation will guide you through a tutorial that introduces the model incrementally and depicts the computation of the order parameter,...


Platforms: Mac

Clusterfunc is a simple and fast method for Clustering functionally divergent genes by Functional Category. Changes in protein structure and function are reflected at the level of amino acid sequence. This principle suggests that functional divergence - changes in protein function, for instance...


Platforms: Mac

Quartz is a full-featured, open source job scheduling service that can be integrated with, or used along side virtually any Java EE or Java SE application - from the smallest stand-alone application to the largest e-commerce system. Quartz can be used to create simple or complex schedules for...


Platforms: *nix

Document clustering project is a data mining suite to cluster a document set. This set of tools was implemented from a series of papers: "Clustering Web Pages Semantically using Combinatorial Topology", "Data mining using granular computing", and "A fast association rule algorithm based on bitmap...


Platforms: *nix

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

QT clustering algorithm as described in:Heyer, L. J., Kruglyak, S., Yooseph, S. (1999). Exploring expression data: Identification and analysis of coexpressed genes. Genome Research 9,...


Platforms: Matlab