π-means: Granular Approach towards Interactive Data Exploration

Abstract

In this paper, we examine the possibility of employing the idea of progressive-inductive (π) aggregation in the k-means algorithm. We base our work on the interactive visualization framework called Skydive which is a tightly coupled system that combines data aggregation and visual aggregation for smooth user interaction. It is based on an aggregate pyramid data structure (π-cube), allowing a visualization client to quickly filter the data. Skydive was designed for interactive data visualization in terms of expressiveness and efficiency. Therefore, the underlying data structure enabled access to data on desired levels of granularity. In this paper, we utilize this concept to speed up the process of clustering proposing π-means - a modified version of the k-means algorithm. This will allow us to explore further possible applications of π-cube towards interactive data exploration.