Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering
This work addresses incremental and real-time clustering challenges for dynamic datasets, though it appears incremental in nature.
The paper tackles the computational and memory limitations of spectral clustering for big data and real-time scenarios by introducing parametric spectral clustering (PSC), which achieves comparable clustering quality with improved efficiency, as demonstrated on various open datasets.
Spectral clustering is a popular method for effectively clustering nonlinearly separable data. However, computational limitations, memory requirements, and the inability to perform incremental learning challenge its widespread application. To overcome these limitations, this paper introduces a novel approach called parametric spectral clustering (PSC). By extending the capabilities of spectral clustering, PSC addresses the challenges associated with big data and real-time scenarios and enables efficient incremental clustering with new data points. Experimental evaluations conducted on various open datasets demonstrate the superiority of PSC in terms of computational efficiency while achieving clustering quality mostly comparable to standard spectral clustering. The proposed approach has significant potential for incremental and real-time data analysis applications, facilitating timely and accurate clustering in dynamic and evolving datasets. The findings of this research contribute to the advancement of clustering techniques and open new avenues for efficient and effective data analysis. We publish the experimental code at https://github.com/109502518/PSC_BigData.