TL;DR
DAOC is a new deterministic clustering algorithm for large networks that produces stable, accurate, and micro-scale clusters by combining Overlap Decomposition and Mutual Maximal Gain, outperforming existing methods.
Contribution
Introduces DAOC, a novel deterministic and agglomerative overlapping clustering algorithm that enhances stability and accuracy in large network analysis.
Findings
DAOC yields 25% more accurate clusters than state-of-the-art algorithms.
It produces stable and reproducible results on synthetic and real-world networks.
The method effectively captures micro-scale clusters in large datasets.
Abstract
Clustering is a crucial component of many data mining systems involving the analysis and exploration of various data. Data diversity calls for clustering algorithms to be accurate while providing stable (i.e., deterministic and robust) results on arbitrary input networks. Moreover, modern systems often operate with large datasets, which implicitly constrains the complexity of the clustering algorithm. Existing clustering techniques are only partially stable, however, as they guarantee either determinism or robustness. To address this issue, we introduce DAOC, a Deterministic and Agglomerative Overlapping Clustering algorithm. DAOC leverages a new technique called Overlap Decomposition to identify fine-grained clusters in a deterministic way capturing multiple optima. In addition, it leverages a novel consensus approach, Mutual Maximal Gain, to ensure robustness and further improve the…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
