A quorum sensing inspired algorithm for dynamic clustering

Abstract

Quorum sensing is a decentralized biological process, through which a community of cells with no global awareness coordinate their functional behaviors based only on cell-medium interactions and local decisions. This paper draws inspiration from quorum sensing and colony competition to derive a new algorithm for data clustering. The algorithm treats each data as a single cell, and uses knowledge of local connectivity to cluster cells into multiple colonies simultaneously. It simulates auto-inducers secretion in quorum sensing to tune the influence radius for each cell. At the same time, sparsely distributed core cells spread their influences to form colonies, and interactions between colonies eventually determine each cell's identity. The algorithm has the flexibility to analyze both static and time-varying data, and its stability and convergence properties are established. The algorithm is tested on several applications, including both synthetic and real benchmarks datasets, alleles clustering, dynamic systems grouping and model identification. Although the algorithm is originally motivated by curiosity about biology-inspired computation, the results suggests that in parallel implementation it performs as well as state-of-the art methods on static data, while showing promising performance on time-varying data such as e.g. clustering robotic swarms.