Empirical Comparison of Sampling Strategies for Classification

Das, Kaberi; Pati, Prem Pujari; Mishra, Debahuti; Panigrahi, Lipismita

doi:10.1016/j.proeng.2012.06.135

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…The data has 10 classes: cytosolic or cytoskeletal, nuclear, mitochondrial, membrane protein, no N-terminal signal, membrane protein-uncleaved, membrane protein-cleaved signal, extracellular, vacuolar, peroxisomal, endoplasmic reticulum lumen. The research in clustering Yeast Dataset is extensively active in recent years [35][36][37] to improve the clustering accuracy. KFCM 29 clustering results based on 10 classes in yeast data are plotted in Fig.…”

Section: Experimental Results On Yeast Datasetmentioning

confidence: 99%

Fuzzy C-Means in Finding Subtypes of Cancers in Cancer Database

Kannan

Ramthilagam²,

Devi

et al. 2014

J. Innov. Opt. Health Sci.

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Finding subtypes of cancer in breast cancer database is an extremely di±cult task because of heavy noise by measurement error. Most of the recent clustering techniques for breast cancer database to achieve cancerous and noncancerous often weigh down the interpretability of the structure. Hence, this paper tries to¯nd e®ective Fuzzy C-Means-based clustering techniques to identify the proper subtypes of cancer in breast cancer database. This paper obtains the objective function of e®ective Fuzzy C-Means clustering techniques by incorporating the kernel induced distance function, Renyi's entropy function, weighted distance measure and neighborhood termsbased spatial context. The e®ectiveness of the proposed methods are proved through the experimental works on Lung cancer database, IRIS dataset, Wine dataset, Checkerboard dataset, Time Series dataset and Yeast dataset. Finally, the proposed methods are implemented successfully to cluster the breast cancer database into cancerous and noncancerous. The clustering accuracy has been validated through error matrix and silhouette method.

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