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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.



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...



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.



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...



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....



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.)



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...



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,...



Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional...



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...



Measure percentage of Accuracy and the Rand index of clustering results
The number of class must equal to the number cluster

Output
Acc = Accuracy of clustering results
rand_index = Rand's Index, measure an agreement of...



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...



a kind of usefull clustering algorithm that is better than kmeans and ward hierarchical clustering algorithms in some data sets



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 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,...



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, 1106d-deOCt1115.



Provide a simple k-mean clustering algorithm in ruby.



Swiftiply is a backend agnostic clustering proxy for web applications that is specifically designed to support HTTP traffic from web frameworks.



In this project, an architecture involving several clustering techniques has to be built like complete-link clustering, group-average agglomerative
clustering and centroid clustering, spectral clustering.



Sequoia is a database clustering middleware offering load balancing and transparent failover. Databases are replicated over multiple nodes and Sequoia balances the queries between them. Sequoia supports online maintenance and recovery operations.