Search Results for "clustering algorithm matlab"

This site provides the source code of two approaches for density-ratio based clustering, used for discovering clusters with varying densities. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. ...

...First, over-training and second, shortage of data for a training of categories. Instead, each TXT file is a category on its own, rather than an assigned category. In a way, this is similar to clustering but not really a clustering algorithm since there is some training involved. The summarizer from Classifier4J has been adjusted to accept two inputs (lets call them A and B). Then, the summarizer gets trained with A to summarize a document B, and vice versa. This extracts a relevant structure for both documents (and thus avoids the over-training) which are then compared using the Vector-Space analysis to give a range of belonging of one document to another (and thus avoids the shortage of information). ...

EToS (Efficient Technology of Spike sorting; Extracellular recording To Spike trains) is an open-source system for spike sorting. EToS contains the programs of spike detection, feature extraction and clustering.

Java package to study a clustering model described in the paper \"Novel Clustering Algorithm Based Upon Games on Evolving Network\" by Q. Li, Z. Chen, Y. He and J-P. Jiang (in arxiv: http://arxiv.org/pdf/0812.5064v1), generalizations and similar issues.

CHAC is a MATLAB toolbox for statistical downscaling.

The Databionics ESOM Tools offer many data mining tasks using Emergent Self-Organizing Maps. Visualization, clustering, and classification of high-dimensional data using databionics principles can be performed interactively or automatically.

This project collect several data mining methods, such as: 1) clustering methods; 2) biclustering methods; 3) FCA methods and etc.