CDC: A Simple Framework for Complex Data Clustering.

Area of Science:

• Data Science
• Machine Learning
• Graph Analytics

Background:

• Exponential growth in data complexity (multiview, non-Euclidean, multirelational) challenges existing clustering methods.
• Current clustering techniques often address specific data challenges in isolation, limiting their applicability.
• Unsupervised knowledge extraction through clustering is vital for practical data analysis.

Purpose of the Study:

• To propose a unified and efficient framework for Complex Data Clustering (CDC).
• To develop a method capable of handling diverse data types with linear complexity.
• To demonstrate the theoretical and experimental effectiveness of the proposed clustering approach.

Main Methods:

• Utilizing graph filtering (GF) to integrate geometric structure and attribute information.
• Employing high-quality, adaptively learned anchors for complexity reduction.
• Applying a novel similarity-preserving (SP) regularizer to maintain data integrity during clustering.

Main Results:

• The proposed Complex Data Clustering (CDC) framework demonstrates linear complexity for efficient data processing.
• Theoretical and experimental validation confirms the cluster-ability of the CDC method.
• Successful deployment of CDC on large-scale graph data (111 million nodes) showcases its practical scalability.

Conclusions:

• Complex Data Clustering (CDC) offers a versatile and efficient solution for modern data challenges.
• The integration of graph filtering and similarity-preserving regularizers enables robust clustering.
• CDC provides a scalable approach for extracting insights from complex, large-volume datasets.