Efficient kernel induced fuzzy c-means based on Gaussian function for imagedata analyzing

Subramanian, S.; Chandrakumar, R. David

doi:10.3233/ifs-151820

Cited by 3 publications

(1 citation statement)

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“…Chen [14] employed fuzzy kernel c-means as basic clustering for network intrusion detection. Senthil and Chandrakumar [15] introduced one kind of kernel fuzzy c-means based on Gaussian function for the purpose of segmentation of medical images. Chen et al [16] used kernel distance fuzzy c-means clustering method to overcome the target-aspect sensitivity in radar high resolution range profile recognition.…”

Section: Introductionmentioning

confidence: 99%

Business Failure Prediction using Fuzzy C-Means with Kernel Method

Wang¹,

Wu²,

Wu³

2017

dtssehs

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Abstract:To propose the new prediction method of Kernel Fuzzy C-Means (KFCM) for business failure. Fuzzy C-Means (FCM) algorithm fails to deal with non-spherical clusters and incomplete data, while the kernel method can map the low-dimensional data into highdimensional feature space which is easier to be separated. Therefore, kernel method is integrated with the FCM to solve the problems of FCM. To fully reflect the performance of different kernel functions, KFCM respectively adopts three kernel functions which include Gaussian, Polynomial, Sigmoid kernel. The paper employs the financial data from Chinese quoted companies to predict the business failure. The prediction outcomes of three KFCMs are not only made a comparison to each other, but also compared with the standard FCM. It can show that KFCMs have better classification accuracy than FCM, but each of them has its advantages for normal and failing companies.

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Section: Introductionmentioning

confidence: 99%