FuBiNFS – fuzzy biclustering neuro-fuzzy system

Simiński, Krzysztof

doi:10.1016/j.fss.2021.07.009

Cited by 9 publications

(2 citation statements)

References 76 publications

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“…Healthcare biclustering use is common in the analysis of gene expression data, as can be seen in the literature. Below, we remember some basic definitions taken from the field of healthcare biclustering [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

Ramkumar¹,

Basker²,

Duraisamy³

et al. 2022

BioMed Research International

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In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods.

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Section: Introductionmentioning

confidence: 99%

Healthcare Biclustering-Based Prediction on Gene Expression Dataset

Ramkumar¹,

Basker²,

Duraisamy³

et al. 2022

BioMed Research International

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“…Biclustering is used in (but is not limited to) bioinformatics. Some examples of biclustering algorithms are HroBi (Michalak and Stawarz, 2013), eBi (Stawarz and Michalak, 2012), or FuBi (Siminski, 2022a).…”

Section: Suchy and K Siminskimentioning

confidence: 99%

GrDBSCAN: A granular density-based clustering algorithm

Suchy,

Siminski

2023

International Journal of Applied Mathematics and Computer Scien

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Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback-its worst-case computational complexity is O(n 2 ) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.

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