CARE: Finding Local Linear Correlations in High Dimensional Data

Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selection, feature transformation (or feature projection) and projected clustering. Being widely used in many applications, these methods aim to capture global patterns and are typically performed in the full feature space. In many emerging biomedical applications, however, scientists are interested in the local latent patterns held by feature subsets, which may be invisible via any global transformation. In this paper, we investigate the problem of finding local linear correlations in high dimensional data. Our goal is to find the latent pattern structures that may exist only in some subspaces. We formalize this problem as finding strongly correlated feature subsets which are supported by a large portion of the data points. Due to the combinatorial nature of the problem and lack of monotonicity of the correlation...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Xiang Zhang, Feng Pan, Wei Wang
• Keywords: Correlated Feature Subsets, Database, ICDE 2008, Local Latent Patterns, Local Linear Correlations