CDC: A Simple Framework for Complex Data Clustering

In today's digital era driven by data, the amount and complexity of the collected data, such as multiview, non-Euclidean, and multirelational, are growing exponentially or even faster. Clustering, which unsupervisedly extracts valid knowledge from data, is extremely useful in practice. However,...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 36; no. 7; pp. 13177 - 13188
Main Authors: Kang, Zhao, Xie, Xuanting, Li, Bingheng, Pan, Erlin
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2025
Subjects:
Anchor graph
Clustering methods
Complexity theory
Laplace equations
large-scale data
Learning systems
multiview learning
Noise
Scalability
Topology
topology structure
Training
Uncertainty
Vectors
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:In today's digital era driven by data, the amount and complexity of the collected data, such as multiview, non-Euclidean, and multirelational, are growing exponentially or even faster. Clustering, which unsupervisedly extracts valid knowledge from data, is extremely useful in practice. However, existing methods are independently developed to handle one particular challenge at the expense of the others. In this work, we propose a simple but effective framework for complex data clustering (CDC) that can efficiently process different types of data with linear complexity. We first use graph filtering (GF) to fuse geometric structure and attribute information. We then reduce complexity with high-quality anchors that are adaptively learned via a novel similarity-preserving (SP) regularizer. We illustrate the cluster-ability of our proposed method theoretically and experimentally. In particular, we deploy CDC to graph data of size 111 M.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2024.3473618