Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

There has been considerable interest in random projections, an approximate algorithm for
estimating distances between pairs of points in a high-dimensional vector space. Let A in Rn …

As computational biologists continue to be inundated by ever increasing amounts of
metagenomic data, the need for data analysis approaches that keep up with the pace of …

In this paper, we address the problem of gesture recognition using the theory of random
projection (RP) and by formulating the whole recognition problem as an 1-minimization …

The technique of latent semantic indexing is used in a wide variety of commercial
applications. In these applications, the processing time and RAM required for SVD …

Computational approaches for synthesizing new chemical compounds have resulted in a
major explosion of chemical data in the field of drug discovery. The quantitative structure …

This paper describes a framework for ontology-based flexible discovery of Semantic Web
services. The proposed approach relies on user-supplied, context-specific map**s from …

A great challenge of text mining arises from the increasingly large text datasets and the high
dimensionality associated with natural language. In this research, a systematic study is …

The use of Vector Space Models (VSM) in the area of Information Retrieval is an established
practice within the scientific community. The reason is twofold: first, its very clean and solid …