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Issue:Clustering stock price volatility using intuitionistic fuzzy sets

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Title of paper: Clustering stock price volatility using intuitionistic fuzzy sets
Author(s):
Georgy Urumov
School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW
w1767944@westminster.ac.uk
Panagiotis Chountas
School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW
p.i.chountas@westminster.ac.uk
Presented at: 25th ICIFS, Sofia, 9—10 September 2022
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3, pages 343–352
DOI: https://doi.org/10.7546/nifs.2022.28.3.343-352
Download:  PDF (914  Kb, File info)
Abstract: 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.
Keywords: K-Means, FCM, IFCM, Intuitionistic fuzzy sets, Volatility of Volatility.
AMS Classification: 03E72, 68T20.
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