Growing Hierarchical Trees for Data Stream clustering and visualization

Abstract

Data stream clustering aims at studying large volumes of data that arrive continuously and the objective is to build a good clustering of the stream, using a small amount of memory and time. Visualization is still a big challenge for large data streams. In this paper we present a new approach using a hierarchical and topological structure (or network) for both clustering and visualization. The topological network is represented by a graph in which each neuron represents a set of similar data points and neighbor neurons are connected by edges. The hierarchical component consists of multiple tree-like hierarchic of clusters which allow to describe the evolution of data stream, and then analyze explicitly their similarity. This adaptive structure can be exploited by descending top-down from the topological level to any hierarchical level. The performance of the proposed algorithm is evaluated on both synthetic and real-world datasets.