Detecting dominant clusters is important in many analytic applications. The
state-of-the-art methods find dense subgraphs on the affinity graph as the
dominant clusters. However, the time and space complexity of those methods are
dominated by the construction of the affinity graph, which is quadratic with
respect to the number of data points, and thus impractical on large data sets.
To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT)
and develop a scalable algorithm, Approximate Localized Infection Immunization
Dynamics (ALID). The major idea is to perform Localized Infection Immunization
Dynamics (LID) to find dense subgraph within local range of the affinity graph.
LID is further scaled up with guaranteed high efficiency and detection quality
by an estimated Region of Interest (ROI) and a carefully designed Candidate
Infective Vertex Search method (CIVS). ALID only constructs small local
affinity graphs and has a time complexity of O(C(a^*+ {\delta})n) and a space
complexity of O(a^*(a^*+ {\delta})), where a^* is the size of the largest
dominant cluster and C << n and {\delta} << n are small constants. We
demonstrate by extensive experiments on both synthetic data and real world data
that ALID achieves state-of-the-art detection quality with much lower time and
space cost on single machine. We also demonstrate the encouraging
parallelization performance of ALID by implementing the Parallel ALID (PALID)
on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours,
achieving a speedup ratio of 7.51 with 8 executors