Cube-space Data Mining

This dissertation demonstrates that cube-space data mining is useful for exploring the huge space of choices in mining, and shows that it can be done in a computationally feasible way. Specifically, we define and characterize this new data-mining paradigm and demonstrate its utility by three novel applications of the paradigm, namely prediction cubes, bellwether analysis and privacy skylines. To meet the computational challenges, for each of these applications, we provide efficient and scalable algorithms by exploiting the structure of the space. Through our techniques and experiments, we show that efficient cube-space data mining on large datasets (which may not fit in memory) is achievable.