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Optimization in Machine Learning and Applications pp 113–126Cite as

A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure Using Fuzzy C-means and Cohort Intelligence

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Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Breast cancer is the most prevailing type of cancer responsible for a large number of deaths every year. However, at the same time, this is largely a curable type of cancer if identified at initial stages. With major advances in research in the areas of image processing, data mining and clustering and machine learning, a more precise prognosis and prediction of breast cancer are possible at earlier stages. A fuzzy clustering model is a popular model used across various researches in image processing to predict the malignancy of breast tumor. The partitional clustering method finds its strength in its fuzzy partitioning such that a data point may belong to different classes with varying degrees of membership (ranging between 0 and 1), which is less rigid as compared to an older and still popular k-means clustering algorithm. The current article attempts to hybridize the fuzzy C-means with the cohort intelligence (CI) algorithm to optimize cluster formation. CI is a robust optimization metaheuristic belonging to the class of socio-inspired optimizers (Kumar M, Kulkarni A Socio-cultural inspired metaheuristics, pp 1–28, Springer International Publishing, 2019 [22]), motivated from self-adapting behavior of candidates in a cohort or a group. CI is typically characterized by its simple algorithmic nature, robust structure and a faster convergence rate, hence gaining popularity. This novel hybridized data clustering algorithm fuzzy-CI imitates the soft clustering and communal learning attitude of clusters and candidates. The hybridized method of fuzzy-CI is validated by testing it on the Breast Cancer Wisconsin (Diagnostic) Dataset. The results validate that the hybridized version exhibits better cluster formation in comparison with the non-hybridized version.

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Author information

Authors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International University, Pune, MH, 412115, India

    Meeta Kumar & Anand J. Kulkarni

  2. Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada

    Anand J. Kulkarni

  3. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, AP, India

    Suresh Chandra Satapathy

Authors
  1. Meeta Kumar
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  2. Anand J. Kulkarni
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  3. Suresh Chandra Satapathy
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Corresponding author

Correspondence to Anand J. Kulkarni .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India

    Anand J. Kulkarni

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

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Kumar, M., Kulkarni, A.J., Satapathy, S.C. (2020). A Hybridized Data Clustering for Breast Cancer Prognosis and Risk Exposure Using Fuzzy C-means and Cohort Intelligence. In: Kulkarni, A., Satapathy, S. (eds) Optimization in Machine Learning and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-0994-0_7

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