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A technology of data fusion and preset algorithms, which is applied in the field of data processing and can solve problems such as increased time complexity
Inactive Publication Date: 2019-04-23
ACADEMY OF MILITARY MEDICAL SCI
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[0004] In view of this, the present application provides a data fusion method and device, which are used to solve the multidimensional attribute data fusion of cancer in the prior art, and carry out molecular typing of cancer, so as to obtain the molecular subtype of cancer. The degree of the problem will increase significantly with the increase of the number of features
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no. 1 example
[0046] See figure 2 , figure 2 A schematic flowchart of the data fusion method provided by the embodiment of the present application is shown. A data fusion method provided in an embodiment of the present application, the method includes:
[0047] Step S100: Connect the obtained similarity matrices to obtain a first heterogeneity matrix.
[0048] Optionally, in the embodiment of the present application, the obtained multiple similarity matrices are connected to obtain the first heterogeneity matrix, including:
[0049] Arranging the plurality of similarity matrices and the plurality of identical preset matrices in a predetermined order to obtain a first heterogeneity matrix.
[0050] Wherein, optionally, in the embodiment of the present application, the multiple similarity matrices include a first similarity matrix and a second similarity matrix;
[0051] The first heterogeneous matrix is:
[0052] Among them, S is the first heterogeneous matrix, I n is the preset matr...
no. 2 example
[0181] See Figure 4 , Figure 4 A schematic structural diagram of the data fusion device provided by the embodiment of the present application is shown. A data fusion device 200 provided in the embodiment of the present application, the data fusion device 200 includes:
[0182] The first heterogeneous matrix obtaining module 210 is configured to connect the obtained similarity matrices to obtain a first heterogeneous matrix.
[0183] The second heterogeneous matrix obtaining module 220 is configured to use a first preset algorithm to iteratively calculate the first heterogeneous matrix, and obtain a second heterogeneous matrix under a steady-state distribution achieved through iteration.
[0184] The fusion similarity matrix obtaining module 230 is configured to use a second preset algorithm to perform fusion calculation on the second heterogeneous matrix to obtain a fusion similarity matrix.
[0185] The multiple community obtaining module 240 is used to perform cluster a...
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Abstract
The invention provides a data fusion method and device which is used for solving the problem that in the prior art, multi-dimensional attribute data fusion is conducted on cancer, the cancer moleculartyping is conducted, and therefore in the process of obtaining the molecular subtype of the cancer, the time complexity can be remarkably increased along with increase of the characteristic number. The method comprises the following steps of connecting a plurality of obtained similarity matrixes to obtain a first heterogeneous matrix; performing iterative computation on the first heterogeneous matrix by using a first preset algorithm to obtain a second heterogeneous matrix under the condition that iteration reaches steady-state distribution; carrying out fusion calculation on the second heterogeneous matrix by using a second preset algorithm to obtain a fusion similarity matrix; and performing clustering analysis on the fusion similarity matrix to obtain a plurality of communities.
Description
technical field [0001] The present application relates to the technical field of data processing, and in particular to a data fusion method and device. Background technique [0002] iCluster is an unsupervised machine learning framework for data aggregation. The method flow is expressed as a matrix factorization of multiple data matrices. Xi represents different data types with the same number of samples, and different data types have different number of features n i . Multiple matrices are decomposed into a common feature space, denoted by matrix Z, also known as cluster indicator matrix; it is used to assign p samples into k communities. The matrices Wi, called coefficient matrices, are specific to each data set i (ie, matrices corresponding to different data types, respectively). The matrix Z captures correlations across data types and assigns samples to subcommunities (cancer molecular subtypes) according to Z. [0003] However, in the actual implementation process,...
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