The framework of relative density-based clustering

Abstract

Density-based clustering, using two-phase scheme which consists of an online component and an offline component, is an effective framework for data stream clustering, it can find arbitrarily shaped clusters and capture the evolving characteristic of real-time data streams accurately. However, the clustering has some deficiencies on offline component. Most algorithm don’t adapt to the unevenly distributed data streams or the multi density distribution of the data streams. Moreover, they only consider the density and centroid to connect the adjacent grid and ignore similarity of attribute value between adjacent grids. In this paper, we calculate the similarity of neighboring grids and take the similarity as a weight that affects the connection of the neighboring grids and propose the relative density-based clustering that cluster the grids based on relative difference model that considers the density, centroid and the weight of similarity between adjacent grids, simply, we connect neighboring grids which are the relative small difference to form clusters on offline component. The experimental results have shown that our algorithm apply to the unevenly distributed data streams and has better clustering quality.