The clustering and classification of fracture orientation data are crucial tasks in geotechnical engineering and rock engineering design. The explicit simulation of fracture orientations is always applied to compensat...
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The clustering and classification of fracture orientation data are crucial tasks in geotechnical engineering and rock engineering design. The explicit simulation of fracture orientations is always applied to compensate for the lack of direct measurements over the entire rock mass. In this study, a single step approach based on the theory of finite mixture models, where the component distributions are Fisher distributions, is proposed for automatic clustering and simulation of fracture orientation data. In the proposed workflow, the spherical k-means algorithm is applied to select the initial cluster centers, and the component-wise expectation-maximization algorithm using the minimum message length criterion is used to automatically determine the optimal number of fracture sets. An additional advantage of the proposed method is the representation of orientation data using a full sphere, instead of the conventional hemispherical characterization. The use of a full spherical representation effectively solves the issue of clustering for fractures with high dip angles. In addition, the calculation process of the mean direction is also simplified. The effectiveness of the model-based clustering method is tested with a complicated artificial data set and two real world data sets. Cluster validity is introduced to evaluate the clustering results. In addition, two other clustering algorithms are also presented for comparison. The results demonstrate that the proposed method can successfully detect the optimal number of clusters, and the parameters of the distributions are well estimated. In addition, the proposed method also exhibits good computational performance.
This paper proposes an intuitive clustering algorithm capable of automatically self-organizing data groups based on the original data structure. Comparisons between the propopsed algorithm and EM [1] and sphericalk-m...
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This paper proposes an intuitive clustering algorithm capable of automatically self-organizing data groups based on the original data structure. Comparisons between the propopsed algorithm and EM [1] and sphericalk-means [7] algorithms are given. These numerical results show the effectiveness of the proposed algorithm, using the correct classification rate and the adjusted Rand index as evaluation criteria [5,6]. In 1995, Mayor and Queloz announced the detection of the first extrasolar planet (exoplanet) around a Sun-like star. Since then, observational efforts of astronomers have led to the detection of more than 1000 exoplanets. These discoveries may provide important information for understanding the formation and evolution of planetary systems. The proposed clustering algorithm is therefore used to study the data gathered on exoplanets. Two main implications are also suggested: (1) there are three major clusters, which correspond to the exoplanets in the regimes of disc, ongoing tidal and tidal interactions, respectively, and (2) the stellar metallicity does not play a key role in exoplanet migration.
Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usual...
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Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usually high-dimensional and sparse. Two approaches for mining typical user profiles, based on matrix dimensionality reduction, are presented. In these approaches, non-negative matrix factorization is applied to reduce dimensionality of the session-URL matrix, and the projecting vectors of the user-session vectors are clustered into typical user-session profiles using the sphericalk -meansalgorithm. The results show that two algorithms are successful in mining many typical user profiles in the user sessions.
To address the fault identification challenge in distribution networks, a method leveraging a mixture of the von Mises-Fisher (mov-MF) distribution model for fault probability identification is proposed. Initially, th...
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To address the fault identification challenge in distribution networks, a method leveraging a mixture of the von Mises-Fisher (mov-MF) distribution model for fault probability identification is proposed. Initially, the synchronous phasor measuring unit is employed to gather the post-fault steady-state voltage phase quantities, and then, the voltage phase angle values are combined to form a three-dimensional feature quantity. Subsequently, the mov-MF distribution model is initialized through the spherical k-means algorithm and the minimum message length algorithm. This model is further refined via the expectation-maximization algorithm to iteratively optimize distribution parameters. The test set data are input into the mov-MF distribution model, which has been constructed using typical fault data, to discern fault types. Finally, the efficacy of the proposed method is validated through simulation verification conducted on the IEEE 33-node distribution system. The analysis of the examples demonstrates the accuracy of the mov-MF distribution model-based fault identification method in identifying single-phase ground, two-phase ground, two-phase interphase, and three-phase short-circuit faults.
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