ALID: Scalable Dominant Cluster Detection

TL;DR

This paper introduces ALID, a scalable algorithm for detecting dominant clusters in large datasets by using localized graph methods and evolutionary game theory, significantly reducing computational costs while maintaining high detection quality.

Contribution

The paper presents ALID, a novel scalable algorithm that efficiently detects dominant clusters using localized affinity graphs and evolutionary game theory, outperforming existing methods in speed and resource usage.

Findings

ALID achieves state-of-the-art detection quality on synthetic and real data.

ALID significantly reduces time and space complexity compared to traditional methods.

Parallel ALID (PALID) processes 50 million data points in 2.29 hours with high speedup.

Abstract

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.