High-dimension data soft and hard clustering integration method based on random subspace
A random subspace, high-dimensional data technology, applied in the field of high-dimensional data soft and hard clustering integration
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[0119] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be further described in detail below in conjunction with the drawings and specific embodiments.
[0120] Attached below figure 1 The steps of the present invention are further described.
[0121] Step 1. Input a high-dimensional data set: input a high-dimensional data set to be clustered, the row vector corresponds to the sample dimension, and the column vector corresponds to the attribute dimension;
[0122] Step 2, data normalization: first obtain the maximum value V(d) corresponding to the attribute of the dth column max and minimum V(d) min , convert the attribute value of column d according to the following formula:
[0123]
[0124] in, It is the i-th data in the d-th column, is the updated value, i∈{1,2,...,n},d∈{1,2,...,D}, n is the number of samples, and D is t...
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