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An improved multi-view spectral clustering based on tissue-like P systems.

Huijian Chen¹, Xiyu Liu²

¹Shandong Normal University, Business School, Jinan, 250385, China.

Scientific Reports

|November 4, 2022

Summary

This study introduces an improved multi-view spectral clustering method using tissue-like P systems. It enhances clustering accuracy and computational efficiency by optimizing similarity matrices and integrating symmetric nonnegative matrix factorization.

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Area of Science:

Computer Science
Artificial Intelligence
Data Mining

Background:

Multi-view spectral clustering is a key technique for data analysis.
Clustering performance heavily relies on the quality of per-view similarity matrices.
Existing methods face challenges in optimizing similarity matrices and computational efficiency.

Purpose of the Study:

To propose an improved multi-view spectral clustering algorithm.
To enhance the quality of per-view similarity matrices.
To improve the computational efficiency of multi-view clustering.

Main Methods:

Developed an iterative approach to generate optimal per-view similarity matrices.
Integrated spectral clustering with symmetric nonnegative matrix factorization (SNMF) for direct clustering output.
Incorporated tissue-like P systems to boost computational efficiency.

Main Results:

The proposed method effectively generates optimal per-view similarity matrices.
Direct clustering output avoids secondary operations like k-means.
Experimental results demonstrate superior performance compared to state-of-the-art algorithms.
Enhanced computational efficiency was observed through integration with tissue-like P systems.

Conclusions:

The novel multi-view spectral clustering algorithm offers improved accuracy and efficiency.
The integration of tissue-like P systems is effective for computational enhancement.
This approach provides a robust solution for multi-view clustering challenges.