CN105160000A - Big data mining method based on dimension reduction - Google Patents
Big data mining method based on dimension reduction Download PDFInfo
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- CN105160000A CN105160000A CN201510566756.7A CN201510566756A CN105160000A CN 105160000 A CN105160000 A CN 105160000A CN 201510566756 A CN201510566756 A CN 201510566756A CN 105160000 A CN105160000 A CN 105160000A
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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Abstract
The invention provides a big data mining method based on dimension reduction and a device executing the method. The method comprises the following steps of: selecting data; preprocessing the data, and executing an information processing process for converting difficult-to-recognize data into easy-to-recognize standard data; performing object analysis on the preprocessed data; converting the preprocessed data into low-dimension data in another space, and using the low-dimension data for replacing preprocessed data to perform subsequent processing; performing value estimation; and performing data mining, and performing evaluation and result feedback and correction. Through the method and the device executing the method, the dimension complexity can be effectively reduced; meanwhile, the analysis completeness and the analysis accuracy are also improved; and valuable information is possibly mined.
Description
Technical field
The present invention relates to electrical data signal breath process field, more specifically, relate to a kind of large data digging method based on dimensionality reduction and system.
Background technology
Along with social industrialization, the improving constantly of the level of IT application, nowadays data have replaced the center calculating the information that becomes and calculate, and cloud computing, large data are becoming a kind of trend and trend.Comprise all many-sides such as memory capacity, availability, I/O performance, data security, extensibility.Large data are data sets that scale is very huge and complicated.Large data have 4V:Volume (in a large number), and data volume increases continuously and healthily; Velocity (at a high speed), data I/O speed is faster; Variety (various), data type and source variation; Value (value), there is the usable value of each side in it.
Because there is the data cell of big data quantity in large data, no doubt, the data cell of these big data quantities has abundant valuable information, is conducive to going deep into the process operations such as mining data value.But the data cell of big data quantity is while increase data value, and too increase the number of dimensions of data, make data volume increase sharply, this is the very real problem faced in this area.Higher dimension and the relevance of data both can make subsequent arithmetic become more complicated, and then greatly reduced processing speed, and under the prerequisite of limited sampling, may cause the reduction of data integrity and accuracy.Therefore to the data cell process of big data quantity and advanced row Data Dimensionality Reduction before analyzing, be a very important job.
But, traditional dimension reduction method and the large data digging method to it, be difficult to meet the complicacy both effectively having reduced dimension, also improve integrality and the accuracy of analysis simultaneously, excavate valuable information as far as possible, in the urgent need to a kind of large data digging method that can effectively solve the problems of the technologies described above in this area.
Summary of the invention
An object of the present invention is to provide a kind of large data digging method based on dimensionality reduction and system, by the method and the device performed in the system of the method, effectively can reduce the complicacy of dimension, also improve integrality and the accuracy of analysis simultaneously, excavate valuable information as far as possible.
The present invention solves the problems of the technologies described above the technical scheme taked to be: a kind of large data digging method based on dimensionality reduction, comprises step: S1: carry out data selection; S2: carry out data prediction, the data transformations performing inconvenience identification is the information process being easy to the authority data identified; S3: carry out object analysis for pretreated data; S4: by pretreated data transformation to another space, concentrates on low-dimensional by the most information of pretreated data, replaces pretreated data to carry out subsequent treatment with low-dimensional data in the space through conversion; S5: carry out Numerical value; And S6: as required, the step based on said process carries out data mining, and carries out assessing and the feedback of result and correction.
According to another aspect of the present invention, wherein data selection is the service data object determining that data mining task relates to, and extracts the data set relevant to mining task according to the requirement of data mining task from related data sources.
According to another aspect of the present invention, wherein in the step of data prediction, based on specification and attribute loop, rough set is adopted to carry out brief, think that the further process of data is below provided convenience, improve performance and also realize better mining effect, and perform abate the noise, missing data process, elimination of duplicate data operation.
According to another aspect of the present invention, the data type wherein analyzing pretreated data at least comprises data block or data segment or individual data.
According to another aspect of the present invention, wherein step S4 comprises step S41: the data after pre-service and analysis are D, and it represents the data array treating a × b of this step process, and wherein a and b is positive integer; Element in the data array of data object D is d
ij, wherein i, j represent row and column sequence number corresponding in array, i and j is the positive integer being less than or equal to a respectively He being less than or equal to b; Through step S4, be a × c array by a × b array transformation, described a × c array can re-construct original data object D in another form, and wherein c is less than b.
According to another aspect of the present invention, if wherein data object D is the data being rich in quantity of information, b is at least 2 times of c, or c is 1, or c at least one order of magnitude less of b.
According to another aspect of the present invention, wherein step S4 comprises step S42 further: by element d
ijbe configured to as calculated: d
ij=[n, i1, i2, i3, i4, m1, m2]
ij, data object D is [N, I1, I2, I3, I4, M1, M2], and wherein N represents the parameter of the type of object, and its data value can carry out assignment and appointment according to data length and array size, and this data value and data length and/or array size are proportionate; I1, I2, I3, I4 represent the parameter in the direction of object, and M1, M2 represent the forward of object, the parameter of reverse transformation model; N=Θ
nΦ
t+ Ξ
n; I1=Θ
i1Φ
t+ Ξ
i1; I2=Θ
i2Φ
t+ Ξ
i2; I3=Θ
i3Φ
t+ Ξ
i3; I4=Θ
i4Φ
t+ Ξ
i4; M1=Θ
m1Φ
t+ Ξ
m1; M2=Θ
m2Φ
t+ Ξ
m2; Wherein Θ
n, Θ
i1, Θ
i2, Θ
i3, Θ
i4, Θ
m1, Θ
m2represent a × c array of mark unit prime component, it is the low-dimensional data in another space; Φ represents the b × c array loading element; And Ξ
n, Ξ
i1, Ξ
i2, Ξ
i3, Ξ
i4, Ξ
m1, Ξ
m2represent the residue value in a × b array; Subscript "
t" represent the transpose operation of array; And including coordinate in each Θ, is the array formed by coordinate.
According to another aspect of the present invention, wherein in Numerical value operation, each item is regarded as the object to be operated represented by the b dimension data in subspace.Estimation function F
b=N
c+ I
c+ M
c,
wherein N '=Θ
nΦ
t, I1 '=Θ
i1Φ
t, I2 '=Θ
i2Φ
t, I3 '=Θ
i3Φ
t, I4 '=Θ
i4Φ
t, M1 '=Θ
m1Φ
t, M2 '=Θ
m2Φ
t; And Ω
1and Ω
2be the diagonal angle array of c × c, it can consider separately the summit of b dimension space.
According to a further aspect of the invention, the device in a kind of system performing step in said method is provided.
Accompanying drawing explanation
By the mode of example instead of by the mode of restriction, embodiments of the invention are shown in the accompanying drawings, wherein:
According to exemplary embodiment of the present invention, Fig. 1 is exemplified with a kind of process flow diagram of the large data digging method based on dimensionality reduction.
Embodiment
In the following description, also several specific embodiment is shown by way of illustration with reference to accompanying drawing.It is to be appreciated that: can imagine and other embodiments can be made and do not depart from the scope of the present disclosure or spirit.Therefore, below describe in detail and should not be considered to have limited significance.
According to exemplary embodiment of the present invention, Fig. 1 is exemplified with a kind of process flow diagram of the large data digging method based on dimensionality reduction.
First, in step sl, data selection is carried out.Data selection is the service data object determining that data mining task relates to, and extracts the data set relevant to mining task according to the requirement of data mining task from related data sources.
Secondly, in step s 2, carry out data prediction, the data transformations performing inconvenience identification is the information process being easy to the authority data identified.In this process, specification and attribute loop are the cores of this process, wherein rough set can be adopted to carry out brief, think that the further process of data is below provided convenience, and improve performance and realize better mining effect; It can perform abate the noise, missing data process, elimination of duplicate data operation.
Secondly, in step s3, object analysis is carried out for pretreated data.Preferably, analyze the data type of pretreated data, include, without being limited to data block or data segment or individual data.Especially, data cell as herein described can refer to the data treating next step process, both can be one or more data block or data segment, individual data, also can be its combination in any.Its scope includes, without being limited to the above content enumerated.
Again, in step s 4 which, by pretreated data transformation to another space, in the space through conversion, the most information of pretreated data is concentrated on low-dimensional, replace pretreated data to carry out subsequent treatment with low-dimensional data.Specifically, above-mentioned steps S4 comprises the following steps: S41, and above-mentioned is D through pre-service and the data after analyzing, and it represents the data array treating a × b of this step process, and wherein a and b is positive integer.Element in the data array of data object D is d
ij, wherein i, j represent row and column sequence number corresponding in array, i and j is the positive integer being less than or equal to a respectively He being less than or equal to b.Through step S4, be a × c array by a × b array transformation, described a × c array can re-construct original data object D in another form, and wherein c is less than b.Preferably, if data object D is the data being rich in quantity of information, b is at least 2 times of c; Preferably, c is 1; Preferably, c at least one order of magnitude less of b.S42, by element d
ijbe configured to as calculated: d
ij=[n, i1, i2, i3, i4, m1, m2]
ij, data object D is [N, I1, I2, I3, I4, M1, M2], and wherein N represents the parameter of the type of object, and its data value can carry out assignment and appointment according to data length and array size, and usually, this data value and data length and/or array size are proportionate; I1, I2, I3, I4 represent the parameter in the direction of object, and M1, M2 represent the forward of object, the parameter of reverse transformation model.Specifically, N=Θ
nΦ
t+ Ξ
n; I1=Θ
i1Φ
t+ Ξ
i1; I2=Θ
i2Φ
t+ Ξ
i2; I3=Θ
i3Φ
t+ Ξ
i3; I4=Θ
i4Φ
t+ Ξ
i4; M1=Θ
m1Φ
t+ Ξ
m1; M2=Θ
m2Φ
t+ Ξ
m2; Wherein Θ
n, Θ
i1, Θ
i2, Θ
i3, Θ
i4, Θ
m1, Θ
m2represent a × c array of mark unit prime component, it is exactly the low-dimensional data in another space; Φ represents the b × c array loading element; And Ξ
n, Ξ
i1, Ξ
i2, Ξ
i3, Ξ
i4, Ξ
m1, Ξ
m2represent the residue value in a × b array; Subscript "
t" represent the transpose operation of array.Including coordinate in each Θ, is the array formed by coordinate.
Again, in step s 5, Numerical value is carried out.In the operation of this Numerical value, each item is regarded as the object to be operated represented by the b dimension data in subspace.Estimation function F
b=N
c+ I
c+ M
c,
Wherein N '=Θ
nΦ
t, I1 '=Θ
i1Φ
t, I2 '=Θ
i2Φ
t, I3 '=Θ
i3Φ
t, I4 '=Θ
i4Φ
t, M1 '=Θ
m1Φ
t, M2 '=Θ
m2Φ
t; And Ω
1and Ω
2be the diagonal angle array of c × c, it can consider separately the summit of b dimension space.By this estimation, attribute constraint condition can be met.
Again, in step s 6, as required, the step based on said process carries out data mining, and carries out assessing and the feedback of result and correction.Data digging method in this step can adopt the method step of this area public office.
Especially, data cell as herein described can refer to pending data, both can be one or more data block or data segment, individual data, also can be its combination in any.Its scope includes, without being limited to the above content enumerated.
By above process, the large data digging method based on dimensionality reduction of the present invention can reduce the complicacy of dimension effectively, also improves integrality and the accuracy of analysis simultaneously, excavates valuable information as far as possible.
It is to be appreciated that: the form of combination of hardware, software or hardware and software example of the present invention and embodiment can be realized.As mentioned above, the main body of this method of any execution can be stored, with the form of volatibility or non-volatile memories, such as memory device, whether no matter erasable picture ROM, maybe can rewrite, or in the form of a memory, such as such as RAM, memory chip, equipment or integrated circuit or on light or the readable medium of magnetic, such as such as CD, DVD, disk or tape.It is to be appreciated that: memory device and storage medium are the examples being suitable for the machine readable storage storing one or more program, upon being performed, described one or more program realizes example of the present invention.Via any medium, such as by the signal of communication that wired or wireless connection is loaded with, example of the present invention can be transmitted electronically, and example suitably comprises identical content.
It is to be noted that because the invention solves above-described technical matters; have employed technician in computing machine and the communications field after reading this description can according to the accessible technological means of its training centre; and obtain described technique effect, so scheme claimed in the following claims belongs to the technical scheme on patent law purposes.In addition, because the claimed technical scheme of claims can manufacture in the industry or use, therefore this technical scheme possesses practicality.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should forgive within protection scope of the present invention.Unless otherwise clearly stated, otherwise disclosed each feature is only the general equivalence of series or an example of similar characteristics.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (10)
1., based on a large data digging method for dimensionality reduction, it is characterized in that comprising the following steps:
S1: carry out data selection;
S2: carry out data prediction, the data transformations performing inconvenience identification is the information process being easy to the authority data identified;
S3: carry out object analysis for pretreated data;
S4: by pretreated data transformation to another space, concentrates on low-dimensional by the most information of pretreated data, replaces pretreated data to carry out subsequent treatment with low-dimensional data in the space through conversion;
S5: carry out Numerical value; And
S6: as required, the step based on said process carries out data mining, and carries out assessing and the feedback of result and correction.
2. the method for claim 1, wherein data selection is the service data object determining that data mining task relates to, and extracts the data set relevant to mining task according to the requirement of data mining task from related data sources.
3. method as claimed in claim 2, wherein in the step of data prediction, based on specification and attribute loop, rough set is adopted to carry out brief, think that the further process of data is below provided convenience, improve performance and also realize better mining effect, and perform abate the noise, missing data process, elimination of duplicate data operation.
4. method as claimed in claim 3, the data type wherein analyzing pretreated data at least comprises data block or data segment or individual data.
5. as the method before as described in arbitrary claim, wherein step S4 comprises step S41: the data after pre-service and analysis are D, and it represents the data array treating a × b of this step process, and wherein a and b is positive integer; Element in the data array of data object D is d
ij, wherein i, j represent row and column sequence number corresponding in array, i and j is the positive integer being less than or equal to a respectively He being less than or equal to b; Through step S4, be a × c array by a × b array transformation, described a × c array can re-construct original data object D in another form, and wherein c is less than b.
6. method, wherein c at least one order of magnitude less of b as claimed in claim 5.
7. the method as described in claim 5 or 6, wherein step S4 comprises step S42 further: by element d
ijbe configured to as calculated: d
ij=[n, i1, i2, i3, i4, m1, m2]
ij, data object D is [N, I1, I2, I3, I4, M1, M2], and wherein N represents the parameter of the type of object, and its data value can carry out assignment and appointment according to data length and array size, and this data value and data length and/or array size are proportionate; I1, I2, I3, I4 represent the parameter in the direction of object, and M1, M2 represent the forward of object, the parameter of reverse transformation model; N=Θ
nΦ
t+ Ξ
n; I1=Θ
i1Φ
t+ Ξ
i1; I2=Θ
i2Φ
t+ Ξ
i2; I3=Θ
i3Φ
t+ Ξ
i3; I4=Θ
i4Φ
t+ Ξ
i4; M1=Θ
m1Φ
t+ Ξ
m1; M2=Θ
m2Φ
t+ Ξ
m2; Wherein Θ
n, Θ
i1, Θ
i2, Θ
i3, Θ
i4, Θ
m1, Θ
m2represent a × c array of mark unit prime component, it is the low-dimensional data in another space; Φ represents the b × c array loading element; And Ξ
n, Ξ
i1, Ξ
i2, Ξ
i3, Ξ
i4, Ξ
m1, Ξ
m2represent the residue value in a × b array; Subscript " T " represents the transpose operation of array; And including coordinate in each Θ, is the array formed by coordinate.
8., as the method before as described in arbitrary claim, wherein in Numerical value operation, each item is regarded as the object to be operated represented by the b dimension data in subspace, and estimation function is F
brelevant with N, I1, I2, I3, I4, M1, M2.
9. method as claimed in claim 8, wherein:
Estimation function F
b=N
c+ I
c+ M
c,
Wherein N '=Θ
nΦ
t, I1 '=Θ
i1Φ
t, I2 '=Θ
i2Φ
t, I3 '=Θ
i3Φ
t, I4 '=Θ
i4Φ
t, M1 '=Θ
m1Φ
t, M2 '=Θ
m2Φ
t; And Ω
1and Ω
2be the diagonal angle array of c × c, it can consider separately the summit of b dimension space.
10., for realizing a system for method any one of claim 1-9, comprise the respective device for realizing each step.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070239753A1 (en) * | 2006-04-06 | 2007-10-11 | Leonard Michael J | Systems And Methods For Mining Transactional And Time Series Data |
| CN103136520A (en) * | 2013-03-25 | 2013-06-05 | 苏州大学 | Shape matching and target recognition method based on PCA-SC algorithm |
| CN104077408A (en) * | 2014-07-11 | 2014-10-01 | 浙江大学 | Distributed semi-supervised content identification and classification method and device for large-scale cross-media data |
| CN104699772A (en) * | 2015-03-05 | 2015-06-10 | 孟海东 | Big data text classifying method based on cloud computing |
-
2015
- 2015-09-08 CN CN201510566756.7A patent/CN105160000B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070239753A1 (en) * | 2006-04-06 | 2007-10-11 | Leonard Michael J | Systems And Methods For Mining Transactional And Time Series Data |
| CN103136520A (en) * | 2013-03-25 | 2013-06-05 | 苏州大学 | Shape matching and target recognition method based on PCA-SC algorithm |
| CN104077408A (en) * | 2014-07-11 | 2014-10-01 | 浙江大学 | Distributed semi-supervised content identification and classification method and device for large-scale cross-media data |
| CN104699772A (en) * | 2015-03-05 | 2015-06-10 | 孟海东 | Big data text classifying method based on cloud computing |
Non-Patent Citations (2)
| Title |
|---|
| 李建林: "一种基于PCA 的组合特征提取文本分类方法", 《计算机应用研究》 * |
| 柯慧燕: "Web文本分类研究及应用", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
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