Mining spatial object associations for scientific data
Hui Yang, Srinivasan Parthasarathy, Sameep Mehta · 2005
In this paper, we present efficient algorithms to discover spatial associations among features extracted from scientific datasets. In contrast to previous work in this area, features are modeled as geometric objects rather than points. We define multiple distance metrics that take into account objects ’ extent. We have developed algorithms to discover two types of spatial association patterns in scientific data. We present experimental results to demonstrate the efficacy of our approach on real datasets drawn from the bioinformatic domain. We also highlight the importance of the discovered patterns by integrating the underlying domain knowledge. 1