Data clustering method and device and computer readable storage medium
A data clustering and clustering technology, which is applied to other database clustering/classification, computer components, computing, etc., can solve the problems of unsatisfactory clustering results and low clustering performance, so as to facilitate popularization and improve clustering Performance, simple and convenient effect
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Embodiment 1
[0028] The embodiment of the present invention proposes a data clustering method, figure 2 Schematic diagram of the implementation process of a data clustering method proposed in the embodiment of this application Figure 1 ,Such as figure 2 As shown, in an embodiment of the present invention, the method for data clustering performed by the data clustering device may include the following steps:
[0029] Step 101, receiving and transforming the original data set.
[0030] In the embodiment of the present application, the data clustering device may first receive the original data set, and perform dimension conversion on the original data set after receiving the original data set.
[0031] Further, in the embodiments of the present application, the original data set can be high-dimensional data, for example, the original data set can be Extended Yale B face data set, Augmented Reality (Augmented Reality, AR) face data set or and high-dimensional data such as handwritten dig...
Embodiment 2
[0085] Based on the first embodiment above, in another embodiment of the present application, when the data clustering device solves the converted third objective function, that is, when solving the above formula (11), it can use the preset auxiliary variable to The converted third objective function is iteratively solved to obtain the representation coefficients.
[0086] Further, in the embodiment of the present application, the data clustering device can introduce preset auxiliary variables J, T∈R n×n , and after introducing the preset auxiliary variable, use the augmented Lagrangian multiplier method to reconstruct, so that the above formula (13) can be obtained, and then the preset auxiliary variable J, preset auxiliary variable T, Z, E , Lagrange multipliers, and μ are updated to obtain the optimal representation coefficient Z * .
[0087] In the embodiment of this application, as an example, for the original data set X=[x 1 ,x 2 ,...,x n ]∈R m×n When determining t...
Embodiment 3
[0108] Based on the first and second embodiments above, in another embodiment of the present application, Figure 4 Schematic diagram of the implementation process of a data clustering method proposed in the embodiment of this application Figure II ,Such as Figure 4 As shown, the data clustering device is based on the similarity matrix, and the method of using spectral clustering to obtain the clustering result corresponding to the original data set may include the following steps:
[0109] Step 301: Obtain a normalized symmetric Laplacian matrix corresponding to the original data set according to the similarity matrix calculation.
[0110] In the embodiment of the present application, after the data clustering device determines the similarity matrix, it can cluster the original data set according to the normalized symmetric spectral clustering algorithm.
[0111] Further, in the embodiment of the present application, the data clustering device may first obtain the normali...
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