An Outlier Detection Algorithm Based on Multimodal Granular Distances

Outlier detection is pivotal in data mining and machine learning, as it focuses on discovering unusual behaviors that deviate substantially from the majority of data samples. Conventional approaches, however, often falter when dealing with complex data that are multimodal or sparse or that exhibit strong nonlinearity. To address these challenges, this paper introduces a novel outlier detection framework named Multimodal Granular Distance-based Outlier Detection (MGDOD), which leverages granular computing principles in conjunction with multimodal granulation techniques. Specifically, similarity measures and granulation methods are employed to generate granules from single-modal data, thereby reducing inconsistencies arising from different data modalities. These granules are then combined to form multimodal granular vectors, whose size, measurement, and operational rules are carefully defined. Building on this conceptual foundation, we propose two multimodal granular distance measures, which are formally axiomatized, and develop an associated outlier detection algorithm. Experimental evaluations on benchmark datasets from UCI, ODDS, and multimodal sources compare the proposed MGDOD method against established outlier detection techniques under various granulation parameters, distance metrics, and outlier conditions. The results confirm the effectiveness and robustness of MGDOD, demonstrating its superior performance in identifying anomalies across diverse and challenging data scenarios.