  Quorum sensing is a decentralized biological process, through which a
community of cells with no global awareness coordinate their functional
behaviors based solely on cell-medium interactions and local decisions. This
paper draws inspirations 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 not only static but also time-varying
data, which surpasses the capacity of many existing algorithms. Its stability
and convergence properties are established. The algorithm is tested on several
applications, including both synthetic and real benchmarks data sets, alleles
clustering, community detection, image segmentation. In particular, the
algorithm's distinctive capability to deal with time-varying data allows us to
experiment it on novel applications such as robotic swarms grouping and
switching model identification. We believe that the algorithm's promising
performance would stimulate many more exciting applications.
