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Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy Means and -Means Algorithms

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  • Yeliz Karaca
  • Carlo Cattani
  • Majaz Moonis
  • Şengül Bayrak

Abstract

Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, -means and fuzzy means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)). Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets’ attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate “insignificant†irregularities while keeping “meaningful†singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities. Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for -means and FCM clustering algorithms. Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms’ accuracy rates.

Suggested Citation

  • Yeliz Karaca & Carlo Cattani & Majaz Moonis & Şengül Bayrak, 2018. "Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy Means and -Means Algorithms," Complexity, Hindawi, vol. 2018, pages 1-15, April.
  • Handle: RePEc:hin:complx:9034647
    DOI: 10.1155/2018/9034647
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    References listed on IDEAS

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    1. Dumitru Baleanu & J. A. Tenreiro Machado & Carlo Cattani & Mihaela Cristina Baleanu & Xiao-Jun Yang, 2014. "Local Fractional Variational Iteration and Decomposition Methods for Wave Equation on Cantor Sets within Local Fractional Operators," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-6, January.
    2. Chen Yi-Ting & Sun Edward W. & Yu Min-Teh, 2015. "Improving model performance with the integrated wavelet denoising method," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 445-467, September.
    3. Pavlov, A.N. & Abdurashitov, A.S. & Sindeeva, O.A. & Sindeev, S.S. & Pavlova, O.N. & Shihalov, G.M. & Semyachkina-Glushkovskaya, O.V., 2016. "Characterizing cerebrovascular dynamics with the wavelet-based multifractal formalism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 149-155.
    4. Nicolis, Orietta & Ramírez-Cobo, Pepa & Vidakovic, Brani, 2011. "2D wavelet-based spectra with applications," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 738-751, January.
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    1. Karaca, Yeliz & Moonis, Majaz & Baleanu, Dumitru, 2020. "Fractal and multifractional-based predictive optimization model for stroke subtypes’ classification," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).

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