Literatura académica sobre el tema "Probabilistic relaxation clustering"
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Artículos de revistas sobre el tema "Probabilistic relaxation clustering"
Szilágyi, László, Szidónia Lefkovits y Sándor M. Szilágyi. "Self-Tuning Possibilistic c-Means Clustering Models". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, Supp01 (5 de noviembre de 2019): 143–59. http://dx.doi.org/10.1142/s0218488519400075.
Texto completoChen, Yuxuan, Mian Liu y Gang Luo. "Complex Temporal Patterns of Large Earthquakes: Devil’s Staircases". Bulletin of the Seismological Society of America 110, n.º 3 (14 de abril de 2020): 1064–76. http://dx.doi.org/10.1785/0120190148.
Texto completoTesis sobre el tema "Probabilistic relaxation clustering"
Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods". University of Sydney, 2008. http://hdl.handle.net/2123/3558.
Texto completoThis thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features of a section of time series. This feature can remove noise from the time series by statistical probability. It is experimentally proven that this feature is successful for pattern repeated time series. Second, a multiscale Gaussian gravity as a pattern relationship measurement which can describe the direction of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that unsupervised pattern classification can be transformed into multiscale supervised pattern classification by multiscale supervisory time series or multiscale filtered time series is presented. The second part of this research focuses on multivariate time series analysis using pattern recognition. A systematic method is proposed to find the independent variables of a group of share prices by time series clustering, principal component analysis, independent component analysis, and object recognition. The number of dependent variables is reduced and the multivariate time series analysis is simplified by time series clustering and principal component analysis. Independent component analysis aims to find the ideal independent variables of the group of shares. Object recognition is expected to recognize those independent variables which are similar to the independent components. This method provides a new clue to understanding the stock market and to modelling a large time series database.
Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods". Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/3558.
Texto completoActas de conferencias sobre el tema "Probabilistic relaxation clustering"
Newman, Timothy S., Jinsoo Lee y Scott R. Vechinski. "Target extraction using hierarchical clustering with refinement by probabilistic relaxation labeling". En Aerospace/Defense Sensing and Controls, editado por Firooz A. Sadjadi. SPIE, 1998. http://dx.doi.org/10.1117/12.323860.
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