CN104850646B - A kind of Frequent tree mining method for digging for single uncertain figure - Google Patents
A kind of Frequent tree mining method for digging for single uncertain figure Download PDFInfo
- Publication number
- CN104850646B CN104850646B CN201510282848.2A CN201510282848A CN104850646B CN 104850646 B CN104850646 B CN 104850646B CN 201510282848 A CN201510282848 A CN 201510282848A CN 104850646 B CN104850646 B CN 104850646B
- Authority
- CN
- China
- Prior art keywords
- subgraph
- uncertain
- single uncertain
- support
- expected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
Landscapes
- Engineering & Computer Science (AREA)
- Library & Information Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of Frequent tree mining method for digging for single uncertain figure, including：Obtain single uncertain figure；Go out all subgraphs of single uncertain figure according to single uncertain enumeration of graph；Calculate separately Expected support of each subgraph on single uncertain figure；Judge whether the subgraph is Frequent tree mining according to Expected support of each subgraph on single uncertain figure；Export all Frequent tree minings.The present invention can use Frequent tree mining digging technology on single uncertain figure, fill up the technological gap of this field by the way that single uncertain figure is divided into multiple means for determining figure and Implication Graph is regarded as to the determining Expected supports for scheming to calculate subgraph contained.
Description
Technical field
The present invention relates to figure digging technologies, particularly, are related to a kind of Frequent tree mining excavation side for single uncertain figure
Method.
Background technology
Uncertainty is in practical application, is all a kind of intrinsic attribute either to endogenous or external source.For example,
In one cooperation social networks, using the information grasped at present, we may not necessarily clearly assert that Bill and two people of Ma Xiu have very
Good cooperative relationship, usual we weigh the possibility of this cooperative relationship using probability.Assuming that general existing for this relationship
Rate is p, and the value of p is manually determined by expert of the art by available information, or is automatically generated by information extraction or create-rule.
In the today in big data epoch, there is more strong demand for management uncertain data, therefore various quality occur at present
The data to differ.Particularly, we are absorbed in the uncertain figure with existing probability on the side of uncertain figure, especially figure.No
Determine that graph model has a wide range of applications field, in addition to community network, uncertain graph model is also applied to communication network, wirelessly
Sensor network, the regulated and control network etc. in the protein Internet and biology.
On the other hand, the theme that Frequent Pattern Mining is paid high attention to as Data Mining, has been continued for last decade,
Correlative study also achieves considerable progress, and wherein Frequent tree mining causes special research interest.So-called Frequent tree mining refers to
Set from multiple small determining figures or the single big subgraph for determining the support found in figure and being not less than user's given threshold value.Frequently
Numerous subgraph is portrayed the data characteristics of determining figure, classification, cluster and is played an important roll in terms of establishing index again.
It is well understood by although the method excavated at present for Frequent tree mining and its on determining figure has had, not
It determines on figure, this problem becomes more interesting but also less studied.Special side weighted graph when one uncertain figure, wherein
Weight in each edge (u, v) is its existing probability.Recently, research work is dedicated to the atlas in multiple small uncertain figures
Upper Mining Frequent subgraph.But although the problem is of equal importance in single large-scale uncertain figure, because real-life big
There is uncertainty more and more in type network --- for example, influence of the people to another person is tool in community network
There is probability；Protein interaction scenario in bio-networks also has certain measurement error --- but the prior art is in present aspect
Blank out.
The problem of for the Frequent tree mining digging technology scheme for single uncertain figure is lacked in the prior art, at present still
Lack effective solution scheme.
Invention content
The problem of for the Frequent tree mining digging technology scheme for single uncertain figure is lacked in the prior art, the present invention
Purpose be to propose a kind of Frequent tree mining method for digging for single uncertain figure, can allow for individually uncertain figure into
Row Frequent tree mining excavates, and has filled up the technological gap of this field.
Based on above-mentioned purpose, technical solution provided by the invention is as follows：
According to an aspect of the invention, there is provided a kind of Frequent tree mining method for digging for single uncertain figure, packet
It includes：
Obtain single uncertain figure；
Go out all subgraphs of single uncertain figure according to single uncertain enumeration of graph；
Calculate separately Expected support of each subgraph on single uncertain figure；
Judge whether the subgraph is Frequent tree mining according to Expected support of each subgraph on single uncertain figure；
Export all Frequent tree minings.
Wherein, going out the single all subgraphs for not knowing figure according to single uncertain enumeration of graph includes：
Multiple Implication Graphs are extracted from single uncertain figure, each Implication Graph is the single uncertain possible presence side of figure
Formula；
Calculate separately all subgraphs that each Implication Graph is included.
Also, the number power on side in the single uncertain figure that the number for extracting multiple Implication Graphs is 2.
Also, it calculates separately Expected support of each subgraph on single uncertain figure and includes：
According to the probability of each edge in single uncertain figure, the existing probability of each Implication Graph is calculated；
One in all subgraphs of specified single uncertain figure；
Calculate separately support of the appointed subgraph in each Implication Graph；
According to the support of the existing probability of each Implication Graph, appointed subgraph in each Implication Graph, calculating is referred to
Expected support of the fixed subgraph in each Implication Graph；
Continue to fix a subgraph from single uncertain figure middle finger and calculate its Expected support in each Implication Graph,
All subgraphs until not knowing figure individually are all designated；
According to Expected support of each subgraph in each Implication Graph, each subgraph is calculated on single uncertain figure
Expected support.
Also, support of the appointed subgraph in each Implication Graph is calculated separately, to use maximum independent set method meter
Calculate support based on minimum image of the appointed subgraph in each Implication Graph.
The Expected support of the above-mentioned each subgraph of basis judges whether the subgraph is that Frequent tree mining includes：
Obtain Expected support threshold value；
Expected support threshold value is compared with Expected support of each subgraph on single uncertain figure respectively；
The subgraph that Expected support of all subgraphs on single uncertain figure is more than to Expected support threshold value is determined as
Frequent tree mining.
From the above it can be seen that technical solution provided by the invention by single uncertain figure by being divided into multiple accumulate
The means of the Expected support for determining figure and being regarded as Implication Graph to determine that figure calculates subgraph contained can make on single uncertain figure
With Frequent tree mining digging technology, the technological gap of this field has been filled up.
Description of the drawings
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the present invention
Example, for those of ordinary skill in the art, without creative efforts, can also obtain according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the flow according to a kind of Frequent tree mining method for digging for single uncertain figure of the embodiment of the present invention
Figure；
Fig. 2 is in a kind of Frequent tree mining method for digging for single uncertain figure according to the embodiment of the present invention, individually
Uncertain figure, the one embodiment for determining figure and subgraph.
Specific implementation mode
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction in the embodiment of the present invention
Attached drawing, technical solution in the embodiment of the present invention further carry out it is clear, complete, describe in detail, it is clear that it is described
Embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field
The every other embodiment that those of ordinary skill is obtained, shall fall within the protection scope of the present invention.
One determines that figure G is a tuple (VG,EG,lG,ΣG), wherein VGIt is node set,It is side
Set, lG:VG∪EG→ΣGIt is for the function of node and side imparting label.|VG| and | EG| G interior joints and side are indicated respectively
Quantity.In order to describe simplicity, it will be assumed that figure is undirected, and does not have self-loopa and Non-manifold edges.But this method can be held very much
Easy is extended to the digraph with Non-manifold edges.
If there is injection f:Vg→VGMeet following two conditions simultaneously：
We just useIt indicates that a subgraph g is isomorphic to and determines figure G.We claim g to be the subgraph of G, and G is the hypergraph of g,
F (g) is an insertions of the g in G.If g is the direct hypergraph of g ',And | Eg|=| Eg′|+1.Direct hypergraph
It refer to the hypergraph of a line only more than subgraph.
ForAnd support threshold τ, it is assumed that there are a function weighs supports of the g in G, then
Most direct idea is the isomorphism number for calculating g in G.However, the support computational methods do not have antimonotone.Antimonotone
Property to be capable of the effectively algorithm in pruning search space for exploitation be very crucial, if without this property, must not be entire
Space carries out exhaustive search.Therefore, current to have researched and proposed many support calculating method methods with antimonotone, including
Minimum reflection method (MI) is harmful to overlay method (HO) and maximum independent set method (MIS).These computational methods are all based on subgraph
Isomorphism, but it is different to prolonging raw overlapping compatibility, and cause computation complexity different.Particularly, MI is that only one can
In the method efficiently calculated, and HO and MIS are directed to solve the problems, such as that NP is complete；It is that MI is obtained the result is that HO/MIS obtains result
Superset, therefore, result can be obtained from the result of MI by further calculating.Therefore, next we use MI conducts
The standard that support calculates, however algorithm only needs simply change that can be extended to other two kinds of computational methods.
Consider a Subgraph Isomorphism set F={ f } from g to G.F (v) indicates that { v ' } exists and reflect wherein for each v '
F is penetrated by v ∈ VgIt is mapped to v ' ∈ VG.Support based on minimum image be expressed as sup (g, G)=Hereafter
In " support " be " support based on minimum image " abbreviation.
Fig. 2 shows determining figure G and subgraph g, which has modeled cooperation social networks, and wherein node table is leted others have a look at, and side indicates
Cooperative relationship.Everyone research field indicates biologist as label, i.e. Bio；For clear, the label quilt on side of diagram
It is omitted.It is easy to find that there are three isomorphisms between g and G, is respectively (u1,u2) arrive (v1,v2),(v3,v2) and (v3,v4).As a result
It is sup (g, G)=min { 2,2 }=2.
Although determining that the importance for scheming upper subgraph is weighed by support, this concept is not anticipated on uncertain figure
Justice, because of existing probability in graph structure, inclusion relation thicken or do not know it is existing be operated in it is multiple small uncertain
Expected support is defined on the atlas of figure, this definition calculates the tribute for not knowing figure support in the determination figure contained
It offers, as long as current subgraph is contained an and extends this concept, we define Expected support on single big uncertain figure and are
The polymerizing value of support on be possible to figure, i.e. support are all by not knowing the probability point on the determination figure that figure contains
Cloth subgraphs are more than that a given threshold value is considered as offsets of the frequent due to definition, at present in the figure of multiple small uncertain figures
Algorithm on collection be no longer desirable for individually uncertain figure we have proposed an efficient solution with accuracy guarantee,
It solves the probability based on side and the support based on point calculates.
According to an embodiment of the invention, a kind of Frequent tree mining method for digging for single uncertain figure is provided.
As shown in Figure 1, the Frequent tree mining method for digging for single uncertain figure provided according to an embodiment of the invention
Including：
Step S101 obtains single uncertain figure；
Step S103 goes out all subgraphs of single uncertain figure according to single uncertain enumeration of graph；
Step S105 calculates separately Expected support of each subgraph on single uncertain figure；
Step S107 judges whether the subgraph is frequent according to Expected support of each subgraph on single uncertain figure
Subgraph；
Step S109 exports all Frequent tree minings.
Wherein, going out the single all subgraphs for not knowing figure according to single uncertain enumeration of graph includes：
Multiple Implication Graphs are extracted from single uncertain figure, each Implication Graph is the single uncertain possible presence side of figure
Formula；
Calculate separately all subgraphs that each Implication Graph is included.
Also, the number power on side in the single uncertain figure that the number for extracting multiple Implication Graphs is 2.
Also, it calculates separately Expected support of each subgraph on single uncertain figure and includes：
According to the probability of each edge in single uncertain figure, the existing probability of each Implication Graph is calculated；
One in all subgraphs of specified single uncertain figure；
Calculate separately support of the appointed subgraph in each Implication Graph；
According to the support of the existing probability of each Implication Graph, appointed subgraph in each Implication Graph, calculating is referred to
Expected support of the fixed subgraph in each Implication Graph；
Continue to fix a subgraph from single uncertain figure middle finger and calculate its Expected support in each Implication Graph,
All subgraphs until not knowing figure individually are all designated；
According to Expected support of each subgraph in each Implication Graph, each subgraph is calculated on single uncertain figure
Expected support.
Also, support of the appointed subgraph in each Implication Graph is calculated separately, to use maximum independent set method meter
Calculate support based on minimum image of the appointed subgraph in each Implication Graph.
The Expected support of the above-mentioned each subgraph of basis judges whether the subgraph is that Frequent tree mining includes：
Obtain Expected support threshold value；
Expected support threshold value is compared with Expected support of each subgraph on single uncertain figure respectively；
The subgraph that Expected support of all subgraphs on single uncertain figure is more than to Expected support threshold value is determined as
Frequent tree mining.
Below according to the specific embodiment technical solution that the present invention is further explained.
One uncertain figure is a tuple Gu=(G, P), wherein G are one and determine figure, P:EG→ (0,1 is a probability
Each edge e is mapped as an existing probability, is indicated by Pe by function, e ∈ EG.G is trunk figure.
Once it is determined that each edge there are situation, by GuIt can contain to obtain and determine figure Gi, referred to as Implication Graph.Therefore one
A uncertain figure GuContain in totalPossible determining figure, each Implication Graph is GuPossible existing way.
It is contemplated that model hypothesis side between existing probability be independent from each other, this model has many actual answer
With, then, GuContain GiProbability or GiExisting probability, can be by including or not calculated including side：
Since the classical probability about support becomes meaningful on uncertain figure, we seek help from expectation and support
Degree, i.e., the probability distribution in Implication Graph.
We are by subgraph g in uncertain figure GuOn Expected support be defined as：
Wherein, GiIt is GuImplication Graph.Given Expected support threshold value σ, subgraph g are if it is frequent, then g is in GuIn
Expected support will be not less than threshold value, i.e. esup (g, Gu)≥σ。
To each GuImplication Graph Gi,esup(g,Gi)≤esup(g′,Gi).Inequality is carried out to i
It is still set up after summation.Therefore, esup (g, Gu)≤esup(g′,Gu), Expected support is antimonotone.
A given uncertain figure Gu=(G, a P) and Expected support threshold value σ, the single uncertain upper Frequent tree mining of figure are dug
Pick problem refers to finding that all Expected supports are not less than the subgraph g of given threshold value, i.e.,
We give the semanteme of Frequent tree mining in the definition of Expected support.Assuming that sup (g, Gu)=10, GrIt indicates
The Implication Graph of one random independent selection, then we have reason it is expected g in GrIn at least 10 unduplicated appearance.According to
Existing analysis is suitable for carrying out motif discovery in uncertain figure based on the semantic Frequent tree mining of expectation.When there is no ambiguous
Property when, below omit field Gu, that is, it is expressed as sup (g).
The algorithm enumerate-evaluated we have proposed one is named as fanta (frequent subgraph mining
on uncertain graphs).Fanta algorithms first enumerate all possible candidate subgraph, then calculate each subgraph and it is expected branch
Then degree of holding decides whether to export as result.Any enumeration strategy that Apriori properties are utilized can use.
Apriori properties state that infrequently the hypergraph of subgraph is also impossible to be frequent.Particularly, all sons in figure are not known
Figure can be organized as a directed acyclic graph (DAG) for having root, wherein node on behalf candidate subgraph (root is expressed as sky).DAG
In an arc from g ' to g indicate that g ' is the direct hypergraph of g.One new side is added to by we every time since frequent side
In Frequent tree mining, be possible to subgraph is enumerated, therefore the subgraph for including n side can be found in the n-th layer of DAG.In order to avoid
Completeness is enumerated while being ensured in repetition, our application method gSpan increase lexicographic order to each subgraph.
In conclusion it is not only frequent to obtain, but also the subgraph with high confidence level in reality, we define in list
On a uncertain figure, the Frequent tree mining Mining Problems based on Expected support, based on the semantic support of expectation for not true
It is very useful to determine motif discovery in network.In order to illustrate the high complexity of this problem, by the way that DNF enumeration problems reduction is asked thus
Topic, it is #P- hardly possiblies to we demonstrate and calculate subgraph Expected support.By means of the above-mentioned technical proposal of the present invention, by will be single
A uncertain figure is divided into the hand of multiple Expected supports for determining figure and being regarded as Implication Graph to determine that figure calculates subgraph contained
Section, we can use Frequent tree mining digging technology on single uncertain figure, fill up the technological gap of this field.
Those of ordinary skills in the art should understand that：The above is only a specific embodiment of the present invention, and
It is not used in the limitation present invention, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done,
It should be included within protection scope of the present invention.
Claims (3)
1. a kind of Frequent tree mining method for digging for single uncertain figure, which is characterized in that including：
Obtain single uncertain figure；
Go out all subgraphs of the single uncertain figure according to the single uncertain enumeration of graph；It is described according to it is described it is single not really
Determine enumeration of graph and go out all subgraphs of the single uncertain figure include：Multiple Implication Graphs are extracted from the individually uncertain figure,
Each Implication Graph is the single uncertain possible existing way of figure；Calculating separately each Implication Graph is included
All subgraphs；Wherein, in the single uncertain figure that the number for extracting multiple Implication Graphs is 2 side for several times
Power；
Calculate separately Expected support of each subgraph on the single uncertain figure；It is described calculate separately it is described each
Expected support of the subgraph on the single uncertain figure include：According to the probability of each edge in the single uncertain figure,
Calculate the existing probability of each Implication Graph；Wherein, the probability of each edge is used to indicate the close of two people's cooperative relationship
Degree；One in all subgraphs of the specified single uncertain figure；The appointed subgraph is calculated separately each to accumulate
Containing the support on figure；According to the existing probability of each Implication Graph, the appointed subgraph in each Implication Graph
Support calculates Expected support of the appointed subgraph in each Implication Graph；Continue from the single uncertain figure
Middle finger fixs a subgraph and calculates its Expected support in each Implication Graph, until owning for the single uncertain figure
Subgraph is all designated；According to Expected support of each subgraph in each Implication Graph, each subgraph is calculated in institute
State the Expected support on single uncertain figure；
Judge whether the subgraph is Frequent tree mining according to Expected support of each subgraph on the single uncertain figure；
Export all Frequent tree minings.
2. a kind of Frequent tree mining method for digging for single uncertain figure according to claim 1, which is characterized in that point
Support of the appointed subgraph in each Implication Graph is not calculated, to use maximum independent set method to calculate described be designated
Support based on minimum image of the subgraph in each Implication Graph.
3. a kind of Frequent tree mining method for digging for single uncertain figure according to any one of claims 1 or 2,
It is characterized in that, judging whether the subgraph is that Frequent tree mining includes according to the Expected support of each subgraph：
Obtain Expected support threshold value；
By the Expected support threshold value respectively with Expected support of each subgraph on the single uncertain figure into
Row comparison；
Expected support of all subgraphs on the single uncertain figure is more than to the son of the Expected support threshold value
Figure is determined as Frequent tree mining.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510282848.2A CN104850646B (en) | 2015-05-28 | 2015-05-28 | A kind of Frequent tree mining method for digging for single uncertain figure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510282848.2A CN104850646B (en) | 2015-05-28 | 2015-05-28 | A kind of Frequent tree mining method for digging for single uncertain figure |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104850646A CN104850646A (en) | 2015-08-19 |
| CN104850646B true CN104850646B (en) | 2018-08-07 |
Family
ID=53850290
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510282848.2A Active CN104850646B (en) | 2015-05-28 | 2015-05-28 | A kind of Frequent tree mining method for digging for single uncertain figure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104850646B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108509452B (en) * | 2017-02-27 | 2022-04-12 | 华为技术有限公司 | Matching graph mining method and device |
| CN108846407B (en) * | 2018-04-20 | 2022-02-08 | 太原理工大学 | Magnetic resonance image classification method based on independent component high-order uncertain brain network |
| CN108595686B (en) * | 2018-05-07 | 2020-10-20 | 华东师范大学 | Method, device, equipment, medium and system for mining approximate frequent graph in single graph |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102073708A (en) * | 2010-12-30 | 2011-05-25 | 东北大学 | Large-scale uncertain graph database-oriented subgraph query method |
| US8661004B2 (en) * | 2012-05-21 | 2014-02-25 | International Business Machines Corporation | Representing incomplete and uncertain information in graph data |
| CN103823985A (en) * | 2014-03-05 | 2014-05-28 | 德州学院 | Calculation method of tree index of uncertain graph |
-
2015
- 2015-05-28 CN CN201510282848.2A patent/CN104850646B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102073708A (en) * | 2010-12-30 | 2011-05-25 | 东北大学 | Large-scale uncertain graph database-oriented subgraph query method |
| US8661004B2 (en) * | 2012-05-21 | 2014-02-25 | International Business Machines Corporation | Representing incomplete and uncertain information in graph data |
| CN103823985A (en) * | 2014-03-05 | 2014-05-28 | 德州学院 | Calculation method of tree index of uncertain graph |
Non-Patent Citations (1)
| Title |
|---|
| 从不确定图中挖掘频繁子图模式;邹兆年等;《软件学报》;20091130;第2967-2971页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104850646A (en) | 2015-08-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Groucutt et al. | Rethinking the dispersal of Homo sapiens out of Africa | |
| Rozenfeld et al. | Network analysis identifies weak and strong links in a metapopulation system | |
| Wu | Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood | |
| Hellmuth et al. | Orthology relations, symbolic ultrametrics, and cographs | |
| CN111008447B (en) | Link prediction method based on graph embedding method | |
| Xiang et al. | An elitism based multi-objective artificial bee colony algorithm | |
| Shah-Hosseini | An approach to continuous optimization by the intelligent water drops algorithm | |
| JP6356789B2 (en) | Main knowledge point recommendation method and system | |
| CN102810113B (en) | A kind of mixed type clustering method for complex network | |
| De et al. | Efficient distributed SGD with variance reduction | |
| CN103942308A (en) | Method and device for detecting large-scale social network communities | |
| WO2018166270A2 (en) | Index and direction vector combination-based multi-objective optimisation method and system | |
| CN104850646B (en) | A kind of Frequent tree mining method for digging for single uncertain figure | |
| Whang et al. | Scalable and memory-efficient clustering of large-scale social networks | |
| KR102425673B1 (en) | How to reorder sequencing data reads | |
| Parry et al. | Multiple optimality criteria support Ornithoscelida | |
| CN103699678A (en) | Hierarchical clustering method and system based on multistage layered sampling | |
| CN109726819A (en) | A kind of method and device for realizing reasoning | |
| Zhang et al. | Revealing dynamic mechanisms of cell fate decisions from single-cell transcriptomic data | |
| CN108052743B (en) | Method and system for determining step approach centrality | |
| CN104866588B (en) | A kind of Frequent tree mining method for digging for single uncertain figure | |
| CN110321377A (en) | A kind of multi-source heterogeneous data true value determines method and device | |
| Haque et al. | A memetic algorithm for community detection by maximising the connected cohesion | |
| Alminas et al. | Phylogeography of Cedros and Tiburón Island mule deer in North America’s desert southwest | |
| CN104899283A (en) | Frequent sub-graph mining and optimizing method for single uncertain graph |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |