| Title of paper:
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Clustering stock price volatility using intuitionistic fuzzy sets
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| Author(s):
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| Georgy Urumov
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| School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW
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| w1767944@westminster.ac.uk
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| Panagiotis Chountas
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| School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW
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| p.i.chountas@westminster.ac.uk
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| Published in:
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Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3, pages 343–352
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| DOI:
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https://doi.org/10.7546/nifs.2022.28.3.343-352
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| Download:
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 PDF (914 Kb, File info)
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| Abstract:
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Clustering involves gathering a collection of objects into homogeneous groups or clusters, such that objects in the same cluster are more similar when compared to objects present in other groups. Clustering algorithms that generate a tree of clusters called dendrogram which can be either divisive or agglomerative. The partitional clustering gives a single partition of objects, with a predefined K number of clusters. The most popular partition clustering approaches are: k-means and fuzzy C-means (FCM). In k-means clustering, data are divided into a number of clusters where data elements belong to exactly one cluster. The k-means clustering works well when data elements are well separable. To overcome the problem of non-separability, FCM and IFCM clustering algorithm were proposed. Here we review the use of FCM/IFCM with reference to the problem of market volatility.
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| Keywords:
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K-Means, FCM, IFCM, Intuitionistic fuzzy sets, Volatility of Volatility.
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| AMS Classification:
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03E72, 68T20.
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