Identifying Information-Rich Subspace Trends in High-Dimensional Data.

Identifying information-rich subsets in high-dimensional spaces and representing them as order revealing patterns (or trends) is an important and challenging research problem in many science and engineering applications. The information quotient of large-scale high-dimensional datasets is significantly reduced by the curse of dimensionality which makes the traditional clustering and association analysis methods unsuitable. Most interesting patterns cannot be revealed using global methods which consider the entire data and feature spaces during their analysis. Identifying some interesting patterns in large scale high-dimensional data is usually accomplished using popular techniques such as dimensionality reduction, feature selection and subspace clustering. Though these methods are successfully able to identify the groupings in the feature subsets and localized neighborhood data subspaces, none of these methods extract the latent patterns that are present in local information-rich sub...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: Chandan K. Reddy, Snehal Pokharkar
• Keywords: Computer Science, Data Subsets, Information-rich Subsets, Local Information-rich Subsets, SDM 2009