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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
Georgy Urumov
School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW
Panagiotis Chountas
School of Computer Science and Engineering, University of Westminster, 115 New Cavendish Street, London W1W 6UW
Presented at: 25th ICIFS, Sofia, 9—10 September 2022
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3, pages 343–352
Download: Download-icon.png PDF (914  Kb, 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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