Pajek Clustering

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. Platforms: Matlab

PrepKit MCSE-2000 70-223, Windows 2000 Clustering Service is an interactive software application that helps you learn, tracks your progress, identifies areas for improvements and simulates the actual exam. This PrepKit contains 3 interactive practice tests with over 92 challenging questions... Platforms: Windows

This is educational software whose purpose it is to clarify the workings of different clustering algorithms by visualising the clustering process in 2D space. This make many possible problems and advantages very clear. Platforms: Windows, Mac, Linux

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

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

Many ant species exhibit the behavior of clustering corpses to form cemeteries. Each ant seems to behave individually, moving randomly in space while picking up or depositing (dropping) corpses. The decision to pick up or drop a corpse is based on local information of the antd-deOaos current... Platforms: Matlab

Iris data set clustering using partitional algorithm. Concepts like loading text document and plotting of 4 Dimensional data with the fourth dimension as the intensity of colour of the plot. I used K means algorithm to update the centres from where we calculate the euclidean distance of the other... Platforms: Matlab

Affinity propagation clustering (AP) is a clustering algorithm proposed in "Brendan J. Frey and Delbert Dueck. 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. AP has two... Platforms: Matlab

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,... Platforms: Matlab

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... Platforms: Matlab

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... Platforms: Matlab

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

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

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 MATLAB.The code is very simple and well documented,... Platforms: Matlab

Measure percentage of Accuracy and the Rand index of clustering resultsThe number of class must equal to the number clusterOutput Acc = Accuracy of clustering results rand_index = Rand's Index, measure an agreement of the clustering results match = 2xk matrix which are the best match of the... 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

SpectralClustering performs one of three spectral clustering algorithms (Unnormalized, Shi & Malik, Jordan & Weiss) on a given adjacency matrix. SimGraph creates such a matrix out of a given set of data and a given distance function.The code has been optimized to be both fast and memory... Platforms: Matlab

The code for the spectral graph clustering concepts presented in the following papers is implemented for tutorial purpose:1. Ng, A., Jordan, M., and Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. In T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Advances in Neural... 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

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