In this article we describe an algorithm for feature selection and gene clustering from high dimensional gene expression data. The method is based on measuring similarity between ...
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
In high dimensional data, the general performance of traditional clustering algorithms decreases. This is partly because the similarity criterion used by these algorithms becomes ...
This paper studies the problem of categorical data clustering, especially for transactional data characterized by high dimensionality and large volume. Starting from a heuristic m...