Issue:Clustering stock price volatility using intuitionistic fuzzy sets - Revision history

Vassia Atanassova at 13:44, 7 September 2022

2022-09-07T13:44:29Z

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Vassia Atanassova at 13:42, 7 September 2022

2022-09-07T13:42:44Z

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			\| conference = 25<sup>th</sup> [[ICIFS]], Sofia, 9—10 September 2022
	\| issue = [[Notes on Intuitionistic Fuzzy Sets/28/3\|Notes on Intuitionistic Fuzzy Sets, Volume 28 (2022), Number 3]], pages 343–352		\| issue = [[Notes on Intuitionistic Fuzzy Sets/28/3\|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		\| doi = https://doi.org/10.7546/nifs.2022.28.3.343-352

Vassia Atanassova at 13:41, 7 September 2022

2022-09-07T13:41:15Z

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	\| file = NIFS-28-3-343-352.pdf		\| file = NIFS-28-3-343-352.pdf
	\| format = PDF		\| format = PDF
	\| size = ~~937~~		\| size = 914
	\| 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.		\| 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.		\| keywords = K-Means, FCM, IFCM, Intuitionistic fuzzy sets, Volatility of Volatility.

Vassia Atanassova at 13:39, 7 September 2022

2022-09-07T13:39:44Z

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	\| format = PDF		\| format = PDF
	\| size = 937		\| size = 937
	\| 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.		\| 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.		\| keywords = K-Means, FCM, IFCM, Intuitionistic fuzzy sets, Volatility of Volatility.
	\| ams = 03E72, 68T20.		\| ams = 03E72, 68T20.

Vassia Atanassova: Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{issue/title | title = Clustering stock price volatility using intuitionistic fuzzy sets | shortcut = nifs/28/3/343-352 }} {{issue/author | author = Georgy Urumov | institution = School of Computer Science and Engineering, University of Westminster | address..."

2022-09-07T13:39:20Z

Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{issue/title | title = Clustering stock price volatility using intuitionistic fuzzy sets | shortcut = nifs/28/3/343-352 }} {{issue/author | author = Georgy Urumov | institution = School of Computer Science and Engineering, University of Westminster | address..."

New page

[[Category:Publications on intuitionistic fuzzy sets|{{PAGENAME}}]]
[[Category:Publications in Notes on IFS|{{PAGENAME}}]]
[[Category:Publications in 2022 year|{{PAGENAME}}]]
{{issue/title
| title = Clustering stock price volatility using intuitionistic fuzzy sets
| shortcut = nifs/28/3/343-352
}}
{{issue/author
| author = Georgy Urumov
| institution = School of Computer Science and Engineering, University of Westminster
| address = 115 New Cavendish Street, London W1W 6UW
| email-before-at = w1767944
| email-after-at = westminster.ac.uk
}}
{{issue/author
| author = Panagiotis Chountas
| institution = School of Computer Science and Engineering, University of Westminster
| address = 115 New Cavendish Street, London W1W 6UW
| email-before-at = p.i.chountas
| email-after-at = westminster.ac.uk
}}
{{issue/data
| issue = [[Notes on Intuitionistic Fuzzy Sets/28/3|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
| file = NIFS-28-3-343-352.pdf
| format = PDF
| size = 937
| 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 = 03E72, 68T20.
| references =
# Atanassov, K. T. (1983). Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia, 1983 (Deposed in Central Science–Technology Library of Bulgaria Academy of Science –1697/84).
# Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Past, Present and Future. In: Wagenknecht, M., & Hampel, R. (eds) 3rd Conference of the European Society for Fuzzy Logic and Technology, Zittau, Germany (10.09.2013–12.09.2003), 12–19.
# Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. PhysicaVerlag, New York.352
# Bucci, A. (2020). Realized Volatility Forecasting with Neural Networks. Journal of Financial Econometrics, 18(3), 502–531.
# Demeterfi, K., Derman, E., Kamal, M., & Zou, J. (1999). More Than You Ever Wanted to Know about Volatility Swaps. Goldman Sachs Quantitative Strategies Research Notes, March 1999.
# Francq, C., & Zakoian, J.-M. (2010). GARCH Models: Structure, Statistical Inference and Financial Applications. 1st edition ed. Chichester: John Wiley & Sons.
# Poon, S.-H., & Granger, C. W. J. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, 41(2), 478–539.
# Pradeepkumar, D., & Ravi, V. (2017). Forecasting financial time series volatility using Particle Swarm Optimisation trained Quantile Regression Neural Network. Applied Soft Computing, 58, 35–52.
# Site, A., Birant, D., & Isik, Z. (2019). Stock Market Forecasting Using Machine Learning Models. IEEE 2019 Innovations in Intelligent Systems and Applications Conference (ASYU) - Izmir, Turkey (31.10.2019 – 02.11.2019), 318–323.
# Sugeno, M., & Terano, T. (1977). A model of learning based on fuzzy information, Kybernetes, 6, 157–166.
# Szmidt, E., & Kacprzyk, J. (1997). [[Issue:On measuring distances between intuitionistic fuzzy sets|On measuring distances between intuitionistic fuzzy sets]]. Notes on Intuitionistic Fuzzy Sets, 3(4), 1–3.
# Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets, Fuzzy Sets and Systems, 114(3), 505–518.
# Szmidt, E., & Kacprzyk, J. (2001). [[Issue:Intuitionistic fuzzy sets in some medical applications|Intuitionistic fuzzy sets in some medical applications]]. Notes on Intuitionistic Fuzzy Sets, 7(4), 58–64.
# Van Lung, H., & Kim, J.-M. (2009). A generalized spatial fuzzy C-means algorithm for medical image segmentation. In: FUZZ-IEEE'09: Proceedings of the 18th international conference on Fuzzy Systems, Jeju Island, Korea, 20.08.2009 – 24.08.2009, 409–414.
# Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems, 158(19), 2095–2117.
# Wang, Z., Xu, Z., Liu, S., & Tang, J. (2011). A netting clustering analysis method under intuitionistic fuzzy environment. Applied Soft Computing, 11(8), 5558–5564.
# Wang, Z., Xu, Z., Liu, S., & Yao, Z. (2014). Direct clustering analysis based on intuitionistic fuzzy implication. Applied Soft Computing, 23, 1–8.

| citations =
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