A Multiscale Clustering Approach for Non‐IID Nominal Data

Abstract

Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent and identically distributed (Non-IID). Aiming at the current research situation, this paper proposes a multiscale clustering framework based on Non-IID nominal data. Firstly, the benchmark-scale dataset is clustered based on coupled metric similarity measure. Secondly, it is proposed to transform the clustering results from benchmark scale to target scale that the two algorithms are named upscaling based on single chain and downscaling based on Lanczos kernel, respectively. Finally, experiments are performed using five public datasets and one real dataset of the Hebei province of China. The results showed that the method can provide us not only competitive performance but also reduce computational cost.

Stroma-associated FSTL3 is a factor of calcium channel-derived tumor fibrosis

“…MEGENA is an innovative co-expression network analytical tool that provides multiple benefits over classical co-expression analytical approaches in effectively generating extensive co-expression plane filtering axes while maintaining gene–gene associations 39 . Fast Planar Filtered Network (PFN) generation is the initial stage of MEGENA analysis, followed by the computational acquirement of relevant gene pairs in PFN, and subsequent PFNs construction accumulated to Multiscale Clustering Analysis (MCA) for additional analyses 40 .…”

Identification and Validation in a Novel Classification of Helicase Patterns for the Prediction of Tumor Proliferation and Prognosis

“… 32 Specifically, fast planar filtered network (PFN) construction is the first step of MEGENA analysis, then significant gene pairs in PFN were obtained computationally, and finally the constructed PFNs are aggregated into multiscale clustering analysis (MCA) for subsequent analysis. 33 …”

“…32 Specifically, fast planar filtered network (PFN) construction is the first step of MEGENA analysis, then significant gene pairs in PFN were obtained computationally, and finally the constructed PFNs are aggregated into multiscale clustering analysis (MCA) for subsequent analysis. 33 We extracted the largest gene module from the co-expression network and converted them into a format readable by Cytoscape software for final analysis as well as visualization. 34 Degree values were calculated and used to rank the genes in the module to identify potential hub genes.…”