CDC: A Simple Framework for Complex Data Clustering

Zhao Kang

doi:10.1109/TNNLS.2024.3473618

CDC: A Simple Framework for Complex Data Clustering

2024-01-01

0

PRE

AI

DOI：10.1109/TNNLS.2024.3473618

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Anchor graph

large-scale data

multiview learning

scalability

topology structure

CDC: A Simple Framework for Complex Data Clustering

Abstract

Journal

Organization