ArticleComputational intelligence and neuroscience2021
A Multiscale Clustering Approach for Non-IID Nominal Data.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Stroma-associated FSTL3 is a factor of calcium channel-derived tumor fibrosis.Scientific reports · 2023Article
- Identification and Validation in a Novel Classification of Helicase Patterns for the Prediction of Tumor Proliferation and Prognosis.Journal of hepatocellular carcinoma · 2022Article
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3 authors.
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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.
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