CN106407455A - Data processing method and device based on graph data mining - Google Patents
Data processing method and device based on graph data mining Download PDFInfo
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- CN106407455A CN106407455A CN201610873708.7A CN201610873708A CN106407455A CN 106407455 A CN106407455 A CN 106407455A CN 201610873708 A CN201610873708 A CN 201610873708A CN 106407455 A CN106407455 A CN 106407455A
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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Abstract
The invention provides a data processing method and device based on graph data mining. A user picture is built based on a relevance relation among graph data mining nodes and a conduction weight, a graph data based mining method from part to whole is proposed, a corresponding weight is further given to a relation of a subgraph in the scheme, and the problem which cannot be solved by a traditional data mining method is solved; and uncertainty of the data is added into an existing graph data mining technology, so that the influence brought by an uncertainty factor in previous research is solved.
Description
Technical field
The present invention relates to the technical field of data processing based on graphical data mining, more particularly, to one kind are based on graphical data mining
Data processing method and device.
Background technology
Existing big data portrait is mostly to be based on data itself, not according to social networks digging user feature, and with this
To enrich portrait.
Content of the invention
It is an object of the invention to provide a kind of data processing method based on graphical data mining and device are it is intended to solve existing
Social networks are not based on come abundant portrait, the social networks profit to customer group based on the data processing method of graphical data mining
With inadequate problem.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of data processing method based on graphical data mining, including：
The user library based on diagram data for the setting, a node in the corresponding diagram data of each user, described user library according to
Family feature is classified to user；
In user library, the similarity searching based on user characteristics is carried out to the association user of user, obtain the pass of this user
Characteristic value on this user characteristics for the combination family；The association user of described user is that on diagram data, node corresponding with user is direct
Or the corresponding user of node being indirectly connected with, according to the minimum number of connecting node between association user and the corresponding node of this user
Mesh determines the associated weights of this association user and this user；
Characteristic value on this user characteristics for the association user according to this user, the association user of this user and this user's
Associated weights, the association user sum of this user, obtain characteristic value on this user characteristics for this user.
On the basis of above-described embodiment, further, described diagram data is divided into multiple subgraphs, the association user of user
Including the association user being located on same subgraph from this user and the association user being located on different subgraphs with this user.
On the basis of above-mentioned any embodiment, further, described association user includes one-level association user and two grades
Association user；Described one-level association user is the corresponding user of node that node corresponding with user is directly connected to；Described two grades
Association user is the corresponding user of node that node corresponding with user passes through that a node connects.
On the basis of above-mentioned any embodiment, further, described user characteristics include sex, the age, hobby,
One or more of income, family status, occupation, educational background, purchasing power, division of life span and consumption demand.
On the basis of above-mentioned any embodiment, further, described user inventory is stored in Cloud Server.
A kind of data processing equipment based on graphical data mining, including：
Setup module, for the user library based on diagram data for the setting；A node in the corresponding diagram data of each user, described
User library is classified to user according to user characteristics；
Search module, for the similarity searching based on user characteristics being carried out to the association user of user in user library,
Obtain the association user of this user characteristic value on this user characteristics；The association user of described user be diagram data on user
The corresponding user of node of corresponding node direct or indirect connection, connects according between association user and the corresponding node of this user
The minimal amount connecing node determines the associated weights of this association user and this user；
Acquisition module, for the association of characteristic value on this user characteristics for the association user according to this user, this user
The associated weights of user and this user, the association user sum of this user, obtain characteristic value on this user characteristics for this user.
On the basis of above-described embodiment, further, described diagram data is divided into multiple subgraphs, the association user of user
Including the association user being located on same subgraph from this user and the association user being located on different subgraphs with this user.
On the basis of above-mentioned any embodiment, further, described association user includes one-level association user and two grades
Association user；Described one-level association user is the corresponding user of node that node corresponding with user is directly connected to；Described two grades
Association user is the corresponding user of node that node corresponding with user passes through that a node connects.
On the basis of above-mentioned any embodiment, further, described user characteristics include sex, the age, hobby,
One or more of income, family status, occupation, educational background, purchasing power, division of life span and consumption demand.
On the basis of above-mentioned any embodiment, further, described user inventory is stored in Cloud Server.
The invention has the beneficial effects as follows：
The invention provides a kind of data processing method based on graphical data mining and device, based on graphical data mining node
Between incidence relation and conduction weight, build user portrait it is proposed that based on diagram data by local arrive entirety method for digging,
Also the relation of subgraph is given corresponding weight in further scheme, solving current traditional data mining method cannot solve
Problem；The uncertainty of data is added in existing graphical data mining technology, ignores uncertain in the past in solving to study
The impact that sexual factor is brought.
Brief description
The present invention is further described with reference to the accompanying drawings and examples.
Fig. 1 shows that a kind of flow process of data processing method based on graphical data mining provided in an embodiment of the present invention is illustrated
Figure；
Fig. 2 shows a kind of structural representation of data processing equipment based on graphical data mining provided in an embodiment of the present invention
Figure.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, below in conjunction with drawings and Examples, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only in order to explain the present invention, not
Limit the present invention.
Specific embodiment one
As shown in figure 1, embodiments providing a kind of data processing method based on graphical data mining, including：
The user library based on diagram data for the setting, a node in the corresponding diagram data of each user, described user library according to
Family feature is classified to user；
In user library, the similarity searching based on user characteristics is carried out to the association user of user, obtain the pass of this user
Characteristic value on this user characteristics for the combination family；The association user of described user is that on diagram data, node corresponding with user is direct
Or the corresponding user of node being indirectly connected with, according to the minimum number of connecting node between association user and the corresponding node of this user
Mesh determines the associated weights of this association user and this user；
Characteristic value on this user characteristics for the association user according to this user, the association user of this user and this user's
Associated weights, the association user sum of this user, obtain characteristic value on this user characteristics for this user.
According to the minimal amount of connecting node between association user and the corresponding node of this user determine this association user with
The associated weights of this user, using introduce weight by the way of to different association users to user the feature on certain user characteristics
Correction is made in impact in value, can more accurately react tendency on this user characteristics for the user.
The embodiment of the present invention, based on the incidence relation between graphical data mining node and conduction weight, builds user's portrait,
Propose the method for digging being arrived entirety based on diagram data by local, solve that current traditional data mining method is insurmountable to ask
Topic；The uncertainty of data is added in existing graphical data mining technology, ignore solve to study in the past in uncertain because
The impact that element is brought.The people for example having 90% in the association user of certain user supports dog, and this use raises on a household basis the possibility of dog also relatively
Height, the embodiment of the present invention can lack user in the case of certain user characteristics value, according to diagram data to this user at this
Characteristic value on user characteristics is made and being estimated.
On the basis of above-described embodiment, further, described diagram data is divided into multiple subgraphs, the association user of user
Including the association user being located on same subgraph from this user and the association user being located on different subgraphs with this user.Do so
Benefit be to give corresponding weight by the relation of subgraph, improve the accuracy of data mining.
The embodiment of the present invention does not limit to the scope of association user, it is preferred that described on the basis of above-described embodiment
Association user can include one-level association user and two grades of association users；Described one-level association user is node corresponding with user
The corresponding user of node being directly connected to；Described two grades of association users are that node corresponding with user passes through a node connection
The corresponding user of node.Described association user can include one-level association user and two grades of association users.Sometimes only one-level is closed
Combination its negligible amounts of family, it may be considered that adding the impact of two grades of association users, user characteristics value is made and estimate.For example certain
The one-level association user of user has 90% people to have a foster dog purpose, and two grades of association users only have 20% foster dog purpose, if
Directly average, discreet value in foster dog purpose for this user can be made to greatly deviate from its actual purpose.
The embodiment of the present invention does not limit to user characteristics, and when user is carried out with feature portrait, described user characteristics can
To include the hobby purposes or income, educational background etc. such as the foster dog purpose in above-described embodiment, in above-mentioned any enforcement
Example on the basis of it is preferred that described user characteristics can include sex, the age, hobby, income, family status, occupation,
One or more of educational background, purchasing power, division of life span and consumption demand.
The embodiment of the present invention does not limit to the storage location of user library, its can be stored in home server it is also possible to
It is stored in Cloud Server, it is preferred that described user library can be stored in Cloud Server on the basis of above-mentioned any embodiment.
By database purchase in Cloud Server, can prevent loss of data from bringing unnecessary loss, alternatively, it is also possible to convenient not
With on equipment, data is processed.
Specific embodiment two
As shown in Fig. 2 embodiments providing a kind of data processing equipment based on graphical data mining, including：
Setup module, for the user library based on diagram data for the setting；A node in the corresponding diagram data of each user, described
User library is classified to user according to user characteristics；
Search module, for the similarity searching based on user characteristics being carried out to the association user of user in user library,
Obtain the association user of this user characteristic value on this user characteristics；The association user of described user be diagram data on user
The corresponding user of node of corresponding node direct or indirect connection, connects according between association user and the corresponding node of this user
The minimal amount connecing node determines the associated weights of this association user and this user；
Acquisition module, for the association of characteristic value on this user characteristics for the association user according to this user, this user
The associated weights of user and this user, the association user sum of this user, obtain characteristic value on this user characteristics for this user.
According to the minimal amount of connecting node between association user and the corresponding node of this user determine this association user with
The associated weights of this user, using introduce weight by the way of to different association users to user the feature on certain user characteristics
Correction is made in impact in value, can more accurately react tendency on this user characteristics for the user.
The embodiment of the present invention, based on the incidence relation between graphical data mining node and conduction weight, builds user's portrait,
Propose the method for digging being arrived entirety based on diagram data by local, solve that current traditional data mining method is insurmountable to ask
Topic；The uncertainty of data is added in existing graphical data mining technology, ignore solve to study in the past in uncertain because
The impact that element is brought.The people for example having 90% in the association user of certain user supports dog, and this use raises on a household basis the possibility of dog also relatively
Height, the embodiment of the present invention can lack user in the case of certain user characteristics value, according to diagram data to this user at this
Characteristic value on user characteristics is made and being estimated.
On the basis of above-described embodiment, further, described diagram data is divided into multiple subgraphs, the association user of user
Including the association user being located on same subgraph from this user and the association user being located on different subgraphs with this user.Do so
Benefit be to give corresponding weight by the relation of subgraph, improve the accuracy of data mining.
The embodiment of the present invention does not limit to the scope of association user, it is preferred that described on the basis of above-described embodiment
Association user can include one-level association user and two grades of association users；Described one-level association user is node corresponding with user
The corresponding user of node being directly connected to；Described two grades of association users are that node corresponding with user passes through a node connection
The corresponding user of node.Described association user can include one-level association user and two grades of association users.Sometimes only one-level is closed
Combination its negligible amounts of family, it may be considered that adding the impact of two grades of association users, user characteristics value is made and estimate.For example certain
The one-level association user of user has 90% people to have a foster dog purpose, and two grades of association users only have 20% foster dog purpose, if
Directly average, discreet value in foster dog purpose for this user can be made to greatly deviate from its actual purpose.
The embodiment of the present invention does not limit to user characteristics, and when user is carried out with feature portrait, described user characteristics can
To include the hobby purposes or income, educational background etc. such as the foster dog purpose in above-described embodiment, in above-mentioned any enforcement
Example on the basis of it is preferred that described user characteristics can include sex, the age, hobby, income, family status, occupation,
One or more of educational background, purchasing power, division of life span and consumption demand.
The embodiment of the present invention does not limit to the storage location of user library, its can be stored in home server it is also possible to
It is stored in Cloud Server, it is preferred that described user library can be stored in Cloud Server on the basis of above-mentioned any embodiment.
By database purchase in Cloud Server, can prevent loss of data from bringing unnecessary loss, alternatively, it is also possible to convenient not
With on equipment, data is processed.
It should be noted that in the case of not conflicting, the embodiment in the present invention and the feature in embodiment can phases
Mutually combine.Although present invention has been a certain degree of description it will be apparent that, in the bar without departing from the spirit and scope of the present invention
The suitable change of each condition under part, can be carried out.It is appreciated that the invention is not restricted to described embodiment, and it is attributed to right and wants
The scope asked, it includes the equivalent of each factor described.
Claims (10)
1. a kind of data processing method based on graphical data mining is it is characterised in that include：
The user library based on diagram data for the setting, a node in the corresponding diagram data of each user, described user library is special according to user
Levy and user is classified；
In user library, the similarity searching based on user characteristics is carried out to the association user of user, obtain the pass combination of this user
Characteristic value on this user characteristics for the family；The association user of described user be on diagram data node corresponding with user directly or
The corresponding user of node connecing in succession, true according to the minimal amount of connecting node between association user and the corresponding node of this user
This association user fixed and the associated weights of this user；
Characteristic value on this user characteristics for the association user according to this user, the association user of this user are associated with this user's
Weight, the association user sum of this user, obtain characteristic value on this user characteristics for this user.
2. the data processing method based on graphical data mining according to claim 1 is it is characterised in that described diagram data is drawn
It is divided into multiple subgraphs, the association user of user is included the association user being located on same subgraph with this user and is located at this user
Association user on different subgraphs.
3. the data processing method based on graphical data mining according to claim 1 and 2 is it is characterised in that described association
User includes one-level association user and two grades of association users；Described one-level association user is that node corresponding with user is directly connected to
The corresponding user of node；Described two grades of association users are that the node that node corresponding with user is connected by a node is corresponding
User.
4. the data processing method based on graphical data mining according to claim 1 and 2 is it is characterised in that described user
Feature includes sex, age, hobby, income, family status, occupation, educational background, purchasing power, division of life span and consumption demand
One or more.
5. the data processing method based on graphical data mining according to claim 1 and 2 is it is characterised in that described user
Stock is stored in Cloud Server.
6. a kind of data processing equipment based on graphical data mining is it is characterised in that include：
Setup module, for the user library based on diagram data for the setting；A node in the corresponding diagram data of each user, described user
Classify to user according to user characteristics in storehouse；
Search module, for carrying out the similarity searching based on user characteristics to the association user of user in user library, obtains
Characteristic value on this user characteristics for the association user of this user；The association user of described user is corresponding to user on diagram data
Node direct or indirect connection the corresponding user of node, according between association user and the corresponding node of this user connect section
The minimal amount of point determines the associated weights of this association user and this user；
Acquisition module, for the association user of characteristic value on this user characteristics for the association user according to this user, this user
With the association user sum of the associated weights of this user, this user, obtain characteristic value on this user characteristics for this user.
7. the data processing method based on graphical data mining according to claim 6 is it is characterised in that described diagram data is drawn
It is divided into multiple subgraphs, the association user of user is included the association user being located on same subgraph with this user and is located at this user
Association user on different subgraphs.
8. the data processing equipment based on graphical data mining according to claim 6 or 7 is it is characterised in that described association
User includes one-level association user and two grades of association users；Described one-level association user is that node corresponding with user is directly connected to
The corresponding user of node；Described two grades of association users are that the node that node corresponding with user is connected by a node is corresponding
User.
9. the data processing equipment based on graphical data mining according to claim 6 or 7 is it is characterised in that described user
Feature includes sex, age, hobby, income, family status, occupation, educational background, purchasing power, division of life span and consumption demand
One or more.
10. the data processing equipment based on graphical data mining according to claim 6 or 7 is it is characterised in that described user
Stock is stored in Cloud Server.
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| CN108629023A (en) * | 2018-05-09 | 2018-10-09 | 北京京东金融科技控股有限公司 | Data digging method, device and computer readable storage medium |
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| CN109871415A (en) * | 2019-01-21 | 2019-06-11 | 武汉光谷信息技术股份有限公司 | A kind of user's portrait construction method, system and storage medium based on chart database |
| CN110489660A (en) * | 2019-07-22 | 2019-11-22 | 武汉大学 | A user economic status portrait method based on social media public data |
| CN110502697A (en) * | 2019-08-26 | 2019-11-26 | 武汉斗鱼网络科技有限公司 | A kind of target user's recognition methods, device and electronic equipment |
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| CN110489660B (en) * | 2019-07-22 | 2020-12-18 | 武汉大学 | A method of profiling users' economic status based on social media public data |
| CN110502697A (en) * | 2019-08-26 | 2019-11-26 | 武汉斗鱼网络科技有限公司 | A kind of target user's recognition methods, device and electronic equipment |
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Application publication date: 20170215 |