Title | Clustering stock price volatility using intuitionistic fuzzy sets |
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Type | Journal article |
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Authors | Urumov, G. and Chountas, P. |
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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-seprability, 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 | K-Means |
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| FCM |
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| IFCM |
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| Intuitionistic fuzzy sets |
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| Volatility of Volatility |
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Journal | Notes on Intuitionistic Fuzzy Sets |
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Journal citation | 28 (3), pp. 343-352 |
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ISSN | 2367-8283 |
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| 1310-4926 |
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Year | 2022 |
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Publisher | Notes on Intuitionistic Fuzzy Sets |
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Publisher's version | License CC BY-SA 4.0 File Access Level Open (open metadata and files) |
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Digital Object Identifier (DOI) | https://doi.org/10.7546/nifs.2022.28.3.343-352 |
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Web address (URL) | https://ifigenia.org/wiki/Issue:Clustering_stock_price_volatility_using_intuitionistic_fuzzy_sets |
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Publication dates |
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Published | Sep 2022 |
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