Lightning Fast Asynchronous Distributed K-Means Clustering
Árpád Berta, István Hegedűs, Róbert Ormándi · SZTE Publicatio Repozitórium (University of Szeged) · 2014
Abstract. One of the most fundamental data processing approach is the clustering. This is even true in distributed architectures. Here, we focus on the problem of designing efficient and fast K-Means approaches which work in fully distributed, asynchronous networks without any central control. We assume that the network has a huge number of computational units (even orders of magnitude more than the number of computational units in a general cloud). Our approaches apply online learning clustering models which take different random walks in the network, while they update themselves using the data points stored by the computational units, and various ensemble techniques combine them to get a faster convergence. We define different instantiations of the general framework that apply various ensemble techniques. We evaluate them empirically against several state-of-the-art distributed baseline algorithms in different computational scenarios. The experiments show that our methods are not only robust against network failure, but they also provide accurate clustering and converge extremely fast. 1