CN107798096A - The data processing method and device in the case where contrasting sequence pattern - Google Patents
The data processing method and device in the case where contrasting sequence pattern Download PDFInfo
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Abstract
The invention discloses one kind in the case where contrasting sequence pattern data processing method and device.Methods described includes：After to multiclass sequence samples data processing, Set-Enumeration Tree is obtained；Judge whether subsequence existing for the sequence that the node in Set-Enumeration Tree includes meets the screening conditions pre-set, obtain judged result；After obtaining judged result and meeting the screening conditions for subsequence, the node and its descendant nodes are all removed from the Set-Enumeration Tree, the Set-Enumeration Tree after being handled；Data processing is carried out to the Set-Enumeration number after processing.
Description
Technical field
The present invention relates to field of information processing, espespecially a kind of data processing method and device in the case where contrasting sequence pattern.
Background technology
Contrasting the operation principle of the efficient mining algorithm of sequence pattern is：Ergodic sequence database generates candidate sequence simultaneously first
Beta pruning, which is carried out, using Apriori properties obtains Frequent episodes.Traversal is given birth to by connecting the Frequent episodes that last time obtains every time
Cheng Xin length adds 1 candidate sequence, then scans each candidate sequence and verifies whether it is Frequent episodes.In practical application
In, can not have for the multi-class efficient mining algorithm of contrast sequence pattern by the sequence pattern that Set-Enumeration Tree ensures to excavate
There is an omission, but it is larger in data set to be analyzed and in the case that sequential element number is more, and efficiency is still relatively low.Therefore, pin
To this problem, the invention provides solution.
The content of the invention
In order to solve the above-mentioned technical problem, the invention provides one kind in the case where contrasting sequence pattern data processing method and dress
Put, it is possible to increase the efficiency of data screening.
In order to reach the object of the invention, the invention provides one kind in the case where contrasting sequence pattern data processing method, including：
After to multiclass sequence samples data processing, Set-Enumeration Tree is obtained；
Judge whether subsequence existing for the sequence that the node in Set-Enumeration Tree includes meets the screening pre-set
Condition, obtain judged result；
After obtaining judged result and meeting the screening conditions for subsequence, by the node and its descendant nodes from described
All removed in Set-Enumeration Tree, the Set-Enumeration Tree after being handled；
Data processing is carried out to the Set-Enumeration number after processing；
Wherein, the screening conditions are given multiclass sequence samples data D, support threshold upper limit α, lower limit β, are spaced about
Beam g and contrast γ, η, the multi-class contrast sequence pattern P of excavation must are fulfilled for following three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes；
Wherein, α ∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1), g>0.
Wherein, methods described also has following features：It is described by the node and its descendant nodes from the Set-Enumeration
After all being removed in tree, methods described also includes：
Obtain the node with the same layer of the node on the Set-Enumeration Tree；
The node of supersequence in same node layer and its child node should also be removed from Set-Enumeration Tree.
Wherein, methods described also has following features：It is described by the node and its descendant nodes from the Set-Enumeration
After all being removed in tree, methods described also includes：
Obtain the First ray pattern that the support in all categories is respectively less than the threshold value pre-set；
Node including the First ray pattern and its descendant nodes are all removed.
Wherein, methods described also has following features：It is described by the node and its descendant nodes from the Set-Enumeration
After all being removed in tree, methods described also includes：
Obtain the second sequence that the support in multi-class contrast sequence pattern in sequence samples set is more than threshold alpha
Pattern, wherein the feature of the sequence pattern is contrast (P | α, g)>γ；
Node including second sequence pattern and its descendant nodes are removed from Set-Enumeration Tree.
One kind data processing equipment in the case where contrasting sequence pattern, including：
First processing module, for after to multiclass sequence samples data processing, obtaining Set-Enumeration Tree；
Whether the first judge module, subsequence existing for the sequence that the node for judging in Set-Enumeration Tree includes are full
The screening conditions pre-set enough, obtain judged result；
First removing module, for after obtaining judged result and meeting the screening conditions for subsequence, by the node with
And its descendant nodes all remove from the Set-Enumeration Tree, the Set-Enumeration Tree after being handled；
Second processing module, for carrying out data processing to the Set-Enumeration number after processing；
Wherein, the screening conditions are given multiclass sequence samples data D, support threshold upper limit α, lower limit β, are spaced about
Beam g and contrast γ, η, the multi-class contrast sequence pattern P of excavation must are fulfilled for following three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes；
Wherein, α ∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1), g>0.
Wherein, described device also has following features：Described device also includes：
First acquisition module, for the node and its descendant nodes all to be removed into it from the Set-Enumeration Tree
Afterwards, the node with the same layer of the node on the Set-Enumeration Tree is obtained；
Second removing module, for also should be from Set-Enumeration by the node of supersequence in same node layer and its child node
Removed in tree.
Wherein, described device also has following features：Described device also includes：
Second acquisition module, for the node and its descendant nodes all to be removed into it from the Set-Enumeration Tree
Afterwards, the First ray pattern that the support in all categories is respectively less than the threshold value pre-set is obtained；
3rd removing module, for the node including the First ray pattern and its descendant nodes all to be removed.
Wherein, described device also has following features：Described device also includes：
Acquisition module, after the node and its descendant nodes are all removed from the Set-Enumeration Tree, obtain
The support in multi-class contrast sequence pattern in sequence samples set is taken to be more than the second sequence pattern of threshold alpha, wherein
The feature of the sequence pattern is contrast (P | α, g)>γ；
Node including second sequence pattern and its descendant nodes are removed from Set-Enumeration Tree.
Embodiment provided by the invention, using screening conditions in the Set-Enumeration Tree after multiclass sequence samples data processing
Node carries out removal operation, realizes and multi-class contrast sequence pattern is efficiently excavated, and removes unrelated redundant data, raising pair
The screening efficiency of data.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
Obtain it is clear that or being understood by implementing the present invention.The purpose of the present invention and other advantages can be by specification, rights
Specifically noted structure is realized and obtained in claim and accompanying drawing.
Brief description of the drawings
Accompanying drawing is used for providing further understanding technical solution of the present invention, and a part for constitution instruction, with this
The embodiment of application is used to explain technical scheme together, does not form the limitation to technical solution of the present invention.
Fig. 1 is the flow chart of the data processing method provided by the invention in the case where contrasting sequence pattern；
Fig. 2 is the schematic diagram of Set-Enumeration Tree provided by the invention；
Fig. 3 is the structure chart of the data processing equipment provided by the invention in the case where contrasting sequence pattern.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with accompanying drawing to the present invention
Embodiment be described in detail.It should be noted that in the case where not conflicting, in the embodiment and embodiment in the application
Feature can mutually be combined.
Can be in the computer system of such as one group computer executable instructions the flow of accompanying drawing illustrates the step of
Perform.Also, although logical order is shown in flow charts, in some cases, can be with suitable different from herein
Sequence performs shown or described step.
Fig. 1 is the flow chart of the data processing method provided by the invention in the case where contrasting sequence pattern.Method bag shown in Fig. 1
Include：
Step 101, after to multiclass sequence samples data processing, obtain Set-Enumeration Tree；
Whether subsequence existing for the sequence that step 102, the node judged in Set-Enumeration Tree include meets to set in advance
The screening conditions put, obtain judged result；
Step 103, after obtaining judged result and meeting the screening conditions for subsequence, the node and its descendants are saved
Point all removes from the Set-Enumeration Tree, the Set-Enumeration Tree after being handled；
Step 104, data processing is carried out to the Set-Enumeration number after processing；
Wherein, the screening conditions are given multiclass sequence samples data D, support threshold upper limit α, lower limit β, are spaced about
Beam g and contrast γ, η, the multi-class contrast sequence pattern P of excavation must are fulfilled for following three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes；
Wherein, α ∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1), g>0；
Embodiment of the method provided by the invention, using screening conditions to the Set-Enumeration after multiclass sequence samples data processing
Tree interior joint carries out removal operation, realizes and multi-class contrast sequence pattern is efficiently excavated, remove unrelated redundant data, carry
The high screening efficiency to data.
Embodiment of the method provided by the invention is described further below：
A kind of data processing method for multi-class contrast sequence pattern is proposed in the present invention, it is proposed that two kinds excellent
Change method.Main contents include it is following some：
1) prioritization scheme minimized based on pattern；
2) optimization based on rare sequence；
First, definition mining condition, specifically include：
Given multiclass sequence samples data D, support threshold upper limit α, lower limit β, spacing constraint g and contrast γ, η (α
∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1)), the multi-class contrast sequence pattern P of excavation must is fulfilled for following
Three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes.
The optimization method of mining algorithm；
The first, pattern minimize prioritization scheme
Specifically, it is described by the node and its descendant nodes from the Set-Enumeration Tree all remove after, it is described
Method also includes：
Obtain the node with the same layer of the node on the Set-Enumeration Tree；
The node of supersequence in same node layer and its child node should also be removed from Set-Enumeration Tree.
First, if the sequence that certain node includes in Set-Enumeration Tree has subsequence and meets that requirement is, by the node
And its descendant nodes all removes；If secondly support of the sequence pattern in all categories is respectively less than given threshold
Value, then illustrate that the pattern does not have representativeness, then all removes the node and its descendant nodes.
The multi-class efficient mining algorithm of contrast sequence pattern generates Set-Enumeration Tree by the way of depth-first, such as schemes
Shown in 2.According to the requirement of multi-class contrast sequence pattern, excavate obtained result and in subsequence is not present.Referring to Fig. 2
In Set-Enumeration Tree, if node ab is multi-class contrast sequence pattern, not only node ab descendant nodes should be moved
Remove.It is that the node of node ab supersequences and its child node also should be from Set-Enumeration Trees in the node of node ab same layer
Remove, efficiency is enumerated to improve.
To sum up, sequence P meets the requirement of multi-class contrast sequence pattern, then if sequence | P ' | -1=| and P |, and
P is P ' subsequence, then removes the node comprising P ' and its child node from Set-Enumeration Tree.
Optimization based on rare sequence
Wherein, it is described by the node and its descendant nodes from the Set-Enumeration Tree all remove after, the side
Method also includes：
Obtain the First ray pattern that the support in all categories is respectively less than the threshold value pre-set；
Node including the First ray pattern and its descendant nodes are all removed.
In addition, it is described by the node and its descendant nodes from the Set-Enumeration Tree all remove after, the side
Method also includes：
Obtain the second sequence that the support in multi-class contrast sequence pattern in sequence samples set is more than threshold alpha
Pattern, wherein the feature of the sequence pattern is contrast (P | α, g)>γ；
Node including second sequence pattern and its descendant nodes are removed from Set-Enumeration Tree.
Contrast (P | α, g) is required in multi-class contrast sequence pattern>γ, i.e., sequence pattern P is just in partial order
Support in row sample set have to be larger than α.But if support thresholds of the sequence P in having sequence sample set more is equal
Less than α.It is probably to have caused by noise to illustrate the sequence pattern, therefore the sequence is unsatisfactory for multi-class contrast sequence pattern
It is required that our sequences are referred to as rare sequence.
The given sequence pattern P and sequence P ' using P as maximum-prefix, under conditions of spacing constraint γ is met, P support
Spend for sup (P, γ | D), P ' support is sup (P ', γ | D), then sup (P, γ | D) >=sup (P ', γ | D).
Prove：For sequence S ∈ D, it is known that：
If P ' meets that γ requires to set up in S, P meets that γ requirements must be set up in S；
From the calculation formula of support：Sup (P, γ | D) >=sup (P ', γ | D)) set up.
The characteristics of from Set-Enumeration Tree, the sequence that node N is included are the maximum-prefix that its child node includes sequence.
To sum up, if the sequence that certain node includes in Set-Enumeration Tree is rare sequence, the node and its descendants are saved
Point removes from probabilistic suffix tree, does not influence the completeness of Result.
Fig. 3 is the structure chart of the data processing equipment provided by the invention in the case where contrasting sequence pattern.Fig. 3 shown device bags
Include：
First processing module 301, for after to multiclass sequence samples data processing, obtaining Set-Enumeration Tree；
First judge module 302, subsequence existing for the sequence that the node for judging in Set-Enumeration Tree includes are
The no screening conditions for meeting to pre-set, obtain judged result；
First removing module 303, for being after subsequence meets the screening conditions, by the node obtaining judged result
And its descendant nodes all removes from the Set-Enumeration Tree, the Set-Enumeration Tree after being handled；
Second processing module 304, for carrying out data processing to the Set-Enumeration number after processing；
Wherein, the screening conditions are given multiclass sequence samples data D, support threshold upper limit α, lower limit β, are spaced about
Beam g and contrast γ, η, the multi-class contrast sequence pattern P of excavation must are fulfilled for following three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes；
Wherein, α ∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1), g>0.
In a device embodiment provided by the invention, described device also includes：
First acquisition module, for the node and its descendant nodes all to be removed into it from the Set-Enumeration Tree
Afterwards, the node with the same layer of the node on the Set-Enumeration Tree is obtained；
Second removing module, for also should be from Set-Enumeration by the node of supersequence in same node layer and its child node
Removed in tree.
In a device embodiment provided by the invention, described device also includes：
Second acquisition module, for the node and its descendant nodes all to be removed into it from the Set-Enumeration Tree
Afterwards, the First ray pattern that the support in all categories is respectively less than the threshold value pre-set is obtained；
3rd removing module, for the node including the First ray pattern and its descendant nodes all to be removed.
In a device embodiment provided by the invention, described device also includes：
Acquisition module, after the node and its descendant nodes are all removed from the Set-Enumeration Tree, obtain
The support in multi-class contrast sequence pattern in sequence samples set is taken to be more than the second sequence pattern of threshold alpha, wherein
The feature of the sequence pattern is contrast (P | α, g)>γ；
Node including second sequence pattern and its descendant nodes are removed from Set-Enumeration Tree.
Device embodiment provided by the invention, using screening conditions to the Set-Enumeration after multiclass sequence samples data processing
Tree interior joint carries out removal operation, realizes and multi-class contrast sequence pattern is efficiently excavated, remove unrelated redundant data, carry
The high screening efficiency to data.
One of ordinary skill in the art will appreciate that all or part of step of above-described embodiment can use computer journey
Sequence flow realizes that the computer program can be stored in a computer-readable recording medium, the computer program exists
(such as system, unit, device) performs on corresponding hardware platform, upon execution, including the step of embodiment of the method it
One or its combination.
Alternatively, all or part of step of above-described embodiment can also realize that these steps can using integrated circuit
To be fabricated to integrated circuit modules one by one respectively, or the multiple modules or step in them are fabricated to single integrated electricity
Road module is realized.So, the present invention is not restricted to any specific hardware and software combination.
Each device/functional module/functional unit in above-described embodiment can be realized using general computing device, it
Can concentrate on single computing device, can also be distributed on the network that multiple computing devices are formed.
Each device/functional module/functional unit in above-described embodiment realized in the form of software function module and as
Independent production marketing in use, can be stored in a computer read/write memory medium.Computer mentioned above
Read/write memory medium can be read-only storage, disk or CD etc..
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should be defined by the protection domain described in claim.
Claims (8)
1. one kind data processing method in the case where contrasting sequence pattern, it is characterised in that including：
After to multiclass sequence samples data processing, Set-Enumeration Tree is obtained；
Judge whether subsequence existing for the sequence that the node in Set-Enumeration Tree includes meets the screening conditions pre-set,
Obtain judged result；
After obtaining judged result and meeting the screening conditions for subsequence, by the node and its descendant nodes from the set
All removed in Enumeration Tree, the Set-Enumeration Tree after being handled；
Data processing is carried out to the Set-Enumeration number after processing；
Wherein, the screening conditions are given multiclass sequence samples data D, support threshold upper limit α, lower limit β, spacing constraint g
And contrast γ, η, the multi-class contrast sequence pattern P of excavation must are fulfilled for following three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes；
Wherein, α ∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1), g>0.
2. according to the method for claim 1, it is characterised in that it is described by the node and its descendant nodes from the set
After all being removed in Enumeration Tree, methods described also includes：
Obtain the node with the same layer of the node on the Set-Enumeration Tree；
The node of supersequence in same node layer and its child node should also be removed from Set-Enumeration Tree.
3. method according to claim 1 or 2, it is characterised in that it is described by the node and its descendant nodes from described
After all being removed in Set-Enumeration Tree, methods described also includes：
Obtain the First ray pattern that the support in all categories is respectively less than the threshold value pre-set；
Node including the First ray pattern and its descendant nodes are all removed.
4. according to the method for claim 1, it is characterised in that it is described by the node and its descendant nodes from the set
After all being removed in Enumeration Tree, methods described also includes：
Obtain the second sequence mould that the support in multi-class contrast sequence pattern in sequence samples set is more than threshold alpha
Formula, wherein the feature of the sequence pattern is contrast (P | α, g)>γ；
Node including second sequence pattern and its descendant nodes are removed from Set-Enumeration Tree.
5. one kind data processing equipment in the case where contrasting sequence pattern, it is characterised in that including：
First processing module, for after to multiclass sequence samples data processing, obtaining Set-Enumeration Tree；
First judge module, whether subsequence meets pre- existing for the sequence that the node for judging in Set-Enumeration Tree includes
The screening conditions first set, obtain judged result；
First removing module, for after obtaining judged result and meeting the screening conditions for subsequence, by the node and its
Descendant nodes all remove from the Set-Enumeration Tree, the Set-Enumeration Tree after being handled；
Second processing module, for carrying out data processing to the Set-Enumeration number after processing；
Wherein, the screening conditions are given multiclass sequence samples data D, support threshold upper limit α, lower limit β, spacing constraint g
And contrast γ, η, the multi-class contrast sequence pattern P of excavation must are fulfilled for following three conditions：
contrast(P|α,g)>γ；
contrast(P|β,g)<η；
Pattern minimizes；
Wherein, α ∈ (0,1), β ∈ (0,1), γ ∈ (0,1), η ∈ (0,1), g>0.
6. device according to claim 5, it is characterised in that described device also includes：
First acquisition module, after the node and its descendant nodes are all removed from the Set-Enumeration Tree, obtain
Take the node with the same layer of the node on the Set-Enumeration Tree；
Second removing module, for also should be from Set-Enumeration Tree by the node of supersequence in same node layer and its child node
Remove.
7. the device according to claim 5 or 6, it is characterised in that described device also includes：
Second acquisition module, after the node and its descendant nodes are all removed from the Set-Enumeration Tree, obtain
The support in all categories is taken to be respectively less than the First ray pattern of the threshold value pre-set；
3rd removing module, for the node including the First ray pattern and its descendant nodes all to be removed.
8. device according to claim 5, it is characterised in that described device also includes：
Acquisition module, after the node and its descendant nodes are all removed from the Set-Enumeration Tree, obtain more
Support in the contrast sequence pattern of classification in sequence samples set is more than the second sequence pattern of threshold alpha, wherein described
The feature of sequence pattern is contrast (P | α, g)>γ；
Node including second sequence pattern and its descendant nodes are removed from Set-Enumeration Tree.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112765469A (en) * | 2021-01-25 | 2021-05-07 | 东北大学 | Method for mining representative sequence mode from Web click stream data |
| CN116910320A (en) * | 2023-09-12 | 2023-10-20 | 北京云枢创新软件技术有限公司 | Hierarchical structure tree node screening method, electronic equipment and medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326426A (en) * | 2016-08-24 | 2017-01-11 | 四川大学 | Comparison sequence pattern mining method by adopting item sets as sequential elements |
-
2017
- 2017-10-27 CN CN201711019546.1A patent/CN107798096A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326426A (en) * | 2016-08-24 | 2017-01-11 | 四川大学 | Comparison sequence pattern mining method by adopting item sets as sequential elements |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112765469A (en) * | 2021-01-25 | 2021-05-07 | 东北大学 | Method for mining representative sequence mode from Web click stream data |
| CN112765469B (en) * | 2021-01-25 | 2023-10-27 | 东北大学 | Method for mining representative sequence mode from Web click stream data |
| CN116910320A (en) * | 2023-09-12 | 2023-10-20 | 北京云枢创新软件技术有限公司 | Hierarchical structure tree node screening method, electronic equipment and medium |
| CN116910320B (en) * | 2023-09-12 | 2023-12-08 | 北京云枢创新软件技术有限公司 | Hierarchical structure tree node screening method, electronic equipment and medium |
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Application publication date: 20180313 |