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A clustering method and device for multi-source heterogeneous data

A multi-source heterogeneous data and clustering method technology, applied in the field of multi-source heterogeneous data clustering methods and devices, can solve problems such as difficult interpretation of clustering results, and achieve efficient and accurate multi-clustering analysis and good clustering performance effect

Inactive Publication Date: 2019-06-21
EZHOU INST OF IND TECH HUAZHONG UNIV OF SCI & TECH +1
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Problems solved by technology

[0005] In view of this, the purpose of the embodiments of the present invention is to provide a multi-source heterogeneous data clustering method and device, which solves the problem that the clustering results in the prior art are difficult to interpret, and can also change the clustering flexibly according to the context object, achieving the effect of providing on-demand services for different applications

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  • A clustering method and device for multi-source heterogeneous data
  • A clustering method and device for multi-source heterogeneous data
  • A clustering method and device for multi-source heterogeneous data

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no. 1 example

[0060] Please refer to figure 1 , In this embodiment, a clustering method for multi-source heterogeneous data is provided, which can be used for clustering multi-source heterogeneous data, including but not limited to network public opinion analysis, major disease analysis, resource recommendation and financial risk forecast etc. Specifically, the method includes the following steps:

[0061] Step S10: Aiming at the multi-source heterogeneous characteristic of the feature space, the feature space is fused to construct the object tensor and the feature space combination vector, and the feature space is more than one.

[0062] Step S20: According to the object tensor, obtain the corresponding feature transfer tensor.

[0063] Step S30: Using a preset multi-relationship attribute combination ranking algorithm to process the feature transfer tensor to obtain a corresponding attribute combination ranking tensor.

[0064] Step S40: Using a preset high-order singular value decompo...

no. 2 example

[0122] Please refer to Figure 5 , based on the same inventive concept, this embodiment also provides a multi-source heterogeneous data clustering device 300, the device 300 includes:

[0123] The object tensor acquisition module 301 is used to construct an object tensor and a feature space combination vector by fusing the feature space for the multi-source heterogeneous characteristics of the feature space, and the feature space is more than one;

[0124] A feature transfer tensor acquisition module 302, configured to obtain a corresponding feature transfer tensor according to the object tensor;

[0125] A ranking module 303, configured to use a preset multi-relationship attribute combination ranking algorithm to process the feature transfer tensor to obtain a corresponding attribute combination ranking tensor;

[0126] The decomposition module 304 is used to decompose the object tensor and the attribute combination ranking tensor by using a preset high-order singular value ...

no. 3 example

[0142] Based on the same inventive idea, such as Image 6 As shown, this embodiment provides a multi-source heterogeneous data clustering device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and operable on the processor 420, and the processor 420 The following steps are implemented when the computer program 411 is executed:

[0143] In view of the multi-source heterogeneous characteristics of the feature space, the feature space is fused to construct the combination vector of the object tensor and the feature space, and the feature space is more than one; according to the object tensor, the corresponding feature transfer tensor is obtained; the preset The multi-relationship attribute combination ranking algorithm processes the feature transfer tensor to obtain the corresponding attribute combination ranking tensor; the object tensor and the attribute combination ranking tensor are processed using a preset high-order singular...

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Abstract

The embodiment of the invention provides a clustering method of multi-source heterogeneous data, which comprises the following steps of: aiming at the multi-source heterogeneous characteristic of a characteristic space, fusing the characteristic space to construct more than one object tensor and characteristic space combination vector; obtaining a corresponding feature transfer tensor according tothe object tensor; processing the feature transfer tensor by adopting a preset multi-relation attribute combination ranking algorithm to obtain a corresponding attribute combination ranking tensor; decomposing the object tensor and the attribute combination ranking tensor by adopting a preset high-order singular value decomposition algorithm to obtain a corresponding core tensor and a factor matrix; calculating according to the feature space combination vector, the core tensor and the factor matrix to obtain a corresponding object similarity matrix; and performing clustering according to theobject similarity matrixes under different feature spaces to obtain a multi-clustering result. The method solves the problem that the clustering result is difficult to interpret in the prior art.

Description

technical field [0001] The present invention relates to the technical fields of data processing and the Internet of Things, in particular, to a clustering method and device for multi-source heterogeneous data. Background technique [0002] With the rapid development of high-tech information technologies such as cloud computing, the Internet of Things, social networks, and new social media, a large number of sensing devices, smart products, network communications, and human knowledge, thinking ability, social relations, and cultural elements in the real world have evolved from many This dimension produces large-scale multi-source heterogeneous data, which has the characteristics of mixed characteristics, diverse modes, complex types, etc., and contains different knowledge and value under different views. In many practical applications, data is collected for multiple analysis tasks, and the data can be clustered to produce different groups according to different needs. [000...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 杨天若赵雅靓孙佳宇
Owner EZHOU INST OF IND TECH HUAZHONG UNIV OF SCI & TECH
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