  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.
