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

Inactive Publication Date: 2017-02-22
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a high-dimensional data soft and hard clustering integration method based on random subspace, which can solve the above three limitations

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  • High-dimension data soft and hard clustering integration method based on random subspace
  • High-dimension data soft and hard clustering integration method based on random subspace
  • High-dimension data soft and hard clustering integration method based on random subspace

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Embodiment Construction

[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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Abstract

The invention discloses a high-dimension data soft and hard clustering integration method based on a random subspace. The method comprises the following steps of (1) inputting a high-dimension data set; (2) performing data normalization; (3) generating the random subspace; (4) performing kmeans and fuzzy cmeans clustering; (5) generating a fusion matrix; (6) using a clustering validity index to obtain an optimum clustering number; (7) constructing a decision attribute set; (8) improving rough set attribute reduction to obtain a simplified fusion matrix; (9) performing consistency function division; (10) obtaining a clustering purification rate. By using the method provided by the invention, the random subspace is used for solving the problem of processing difficulty of high-dimension data; the combination of soft clustering and hard clustering is used; original data and intermediate result information are sufficiently utilized for performing the intermediate result redundant attribute reduction; the clustering accuracy is improved; meanwhile, the clustering speed is also accelerated; the problems of incapability of sufficiently utilizing clustering information and removing redundant information in the prior art are solved.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a method for integrating soft and hard clustering of high-dimensional data based on random subspaces. Background technique [0002] Different data sources adopt different clustering algorithms, and different clustering results will be obtained. The effect of using the clustering integration framework to form a unified result of this clustering result is remarkable, and it has attracted more and more attention and research from the academic community. The method of clustering integration has been successfully applied in the field of data mining, such as noise data mining, heterogeneous data mining, data distribution mining, classification data mining and time series data mining, etc. It also has good applications in biological information, information retrieval, decision-making and image processing. Currently, Yu et al. proposed different clustering integration frameworks, such a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 余志文陈洁彦马帅韩国强
Owner SOUTH CHINA UNIV OF TECH
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