CN106897356A - Improved Fuzzy C mean algorithm realizes that search engine keywords optimize - Google Patents
Improved Fuzzy C mean algorithm realizes that search engine keywords optimize Download PDFInfo
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Abstract
Improved Fuzzy C mean algorithm realizes that search engine keywords optimize, and kernel keyword, the corresponding data item of search keyword, such as national monthly volumes of searches, degree of contention and each clicking cost of estimation are determined according to business event（CPC）Deng, dimension-reduction treatment again is carried out to above-mentioned keyword set, each keyword is represented with First Five-Year Plan dimensional vector, increase homepage webpage number and total searched page number, and then the four-dimension is reduced to again by five dimensions, using improved Fuzzy C mean algorithm, initialize random Subject Matrix, structure is subordinate to constraints, and synthesizing overall target function builds the m equation with Lagrangian, finally calculates satisfactionMaximum necessary condition、, according toConvergence make iteration stopping, while have found optimal cluster result, more preferably, run time complexity is low can be with fast lifting keyword ranking for the cluster result degree of accuracy of the present invention.
Description
Technical field
The present invention relates to Semantic Web technology field, and in particular to improved Fuzzy C-Means Algorithm realizes that search engine is closed
Keyword optimizes.
Background technology
Because user is in Internal retrieval information, common means are scanned for using search engine, and user is to searching
The focus of rope results page is focused at the Search Results of nature ranking not in the link area of paid promotion, therefore
Scanning for engine optimization just becomes particularly important improving nature ranking.For most of enterprise, engine is scanned for
Optimization is less investment, returns marketing behavior high, and search engine optimization plays important work for the network marketing of enterprise web site
With.Search engine optimization technology includes black cap technology and white cap technology, and current each search engine has been incorporated into correlation technique and rule
Then the website using black cap technology is punished；White cap technology then represents the optimisation technique of searched engine accreditation.One business
Industry website obtains nature ranking preferentially with its core keyword in main flow search engine, in the business community of today, there is non-
Than ordinary value.Therefore keyword is also commonly known as being the whole foundation stone for searching for application.Whether the use of keyword is proper, directly
The position for being related to website in the Search Results of search engine is connect, now, an effective method is not proposed temporarily to simplify pass
Keyword analysis process, also neither one perfect mechanism manage keyword optimisation strategy and progress, based on the demand, this hair
It is bright to realize that search engine keywords optimize there is provided improved Fuzzy C-Means Algorithm.
The content of the invention
It is directed to keyword optimization and realizes the technical problem of search engine optimization, the invention provides improved Fuzzy C-
Value-based algorithm realizes that search engine keywords optimize.
In order to solve the above problems, the present invention is achieved by the following technical solutions：
Step 1：Kernel keyword is determined according to business event, related keyword is collected using search engine, these are crucial
Word has corresponding data items in a search engine, such as national monthly volumes of searches, degree of contention and each clicking cost (CPC) of estimation
Step 2：With reference to enterprise product and market analysis, the above-mentioned related keyword set for searching of dimensionality reduction is screened；
Step 3：For the keyword set after screening dimensionality reduction, by the corresponding page of search engine search keyword, this
In record homepage webpage number and total searched page number, i.e. each keyword dimensionality reduction be four-dimensional again by five dimensional vectors.
Step 4：Improved Fuzzy C-Means Algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is as follows：
Step 4.1：Initialization data object set D is C classes, and Subject Matrix J is initialized with the random number between value [0,1],
The whole constraints for being subordinate to its satisfaction
Step 4.2：Initialize the similarity function in each ε fieldBuild C class catalogue scalar functions
Step 4.3：According to the result of decision function Δ (S), Ge Cu centers are recalculated；
Step 4.4：Use wijNew Subject Matrix J is calculated, then goes to step 4.2, otherwise iteration terminates, output cluster knot
Really.
Step 5：According to enterprise's concrete condition, comprehensive keyword efficiency optimization and value rate optimize, and selection is suitable crucial
Word optimisation strategy reaches web information flow target.
Present invention has the advantages that：
1, this algorithm can simplify key word analysis flow, and then reduce whole web information flow workload.
2, the run time complexity of this algorithm is low, and processing speed is faster.
3rd, this algorithm has bigger value.
4th, the ranking of website its keyword of fast lifting in a short time can be helped.
5th, for enterprise web site brings certain flow and inquiry, so as to reach preferable web information flow target.
6th, this algorithm is accurately assigned with the weight coefficient of each field density and similarity, and the degree of accuracy of classification results is more
Meet empirical value.
7th, the cluster result signal to noise ratio that this algorithm is obtained is more preferable.
8, it is to avoid result Premature Convergence.
Brief description of the drawings
The improved Fuzzy C-Means Algorithms of Fig. 1 realize that search engine keywords optimize structure flow chart
Applicating flow chart of the improved Fuzzy C-Means Algorithms of Fig. 2 in cluster analysis
Specific embodiment
In order to solve the technical problem that search engine optimization is realized in keyword optimization, the present invention is carried out with reference to Fig. 1-Fig. 2
Describe in detail, its specific implementation step is as follows：
Step 1：Kernel keyword is determined according to business event, related keyword is collected using search engine, these are crucial
Word has corresponding data items in a search engine, such as national monthly volumes of searches, degree of contention and each clicking cost (CPC) of estimation
Deng.
Step 2：With reference to enterprise product and market analysis, the above-mentioned related keyword set for searching of dimensionality reduction is screened；
Step 3：For the keyword set after screening dimensionality reduction, by the corresponding page of search engine search keyword, this
In record homepage webpage number and total searched page number, i.e. each keyword dimensionality reduction be four-dimensional, its specific meter again by five dimensional vectors
Calculation process is as follows：
Here associative key number is m, existing following m × 5 matrix：
Ni、Ldi、CPCi、NiS、NiYIt is followed successively by monthly volumes of searches, degree of contention, the estimation of i-th corresponding this country of keyword
Each clicking cost (CPC), homepage webpage number, total searched page number.
Dimensionality reduction is the four-dimension again, i.e.,
XI ∈ (1,2 ..., m)It is search efficiency, ZI ∈ (1,2 ..., m)It is value rate, as following formula：
Step 4：Improved Fuzzy C-Means Algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is as follows：
Step 4.1：Initialization data object set D is C classes, and Subject Matrix J is initialized with the random number between value [0,1],
The whole constraints for being subordinate to its satisfaction, its specific calculating process is as follows：
C classes are divided into according to ε fields initialization data object set D；
Initialization Subject Matrix J is m × C：
wijBelong to for keyword i the degree coefficient of j classes, i.e. j ∈ (1,2 ..., C), i ∈ (1,2 ..., m).
The whole constraints being subordinate to is：
Step 4.2：Initialize the similarity function in each ε fieldBuild C class catalogue scalar functionsIts specific calculating process is as follows：
Above formula nεIt is the number of data object in each ε field,It is i-th pass in each ε field
Keyword vectorWith the similarity of its central point vector, α, β are respectively quantity nε, similarity function's
Influence coefficient, and alpha+beta=1, α ＜ β, its value can go out suitable value according to experiment iteration.
To belong to the space vector of i-th keyword of j classes,It is j class cluster center vectors, h is vectorial corresponding element
The number of element.
Build C class catalogue scalar functionsFor：
λ1, λ2..., λmIt is the n Lagrange multiplier of constraint formula.To all input parameter derivations, you can must reach above formula
To maximum necessary condition, i.e. wij、CjValue；
Above formulaVector corresponding to keyword i；
Step 4.3：According to the result of decision function Δ (S), Ge Cu centers are recalculated, its specific calculating process is as follows：
The global similarity function that current iteration is obtained,For preceding
The global similarity function that an iteration is obtained, when the iteration result difference for obtaining meets above formula decision condition, then algorithm stops.
Step 4.4：Use wijNew Subject Matrix J is calculated, then goes to step 4.2, otherwise iteration terminates, output cluster knot
Really.
Concrete structure flow such as Fig. 2 of improved Fuzzy C-Means Algorithm.
Step 5：According to enterprise's concrete condition, comprehensive keyword efficiency optimization and value rate optimize, and selection is suitable crucial
Word optimisation strategy reaches web information flow target.
Improved Fuzzy C-Means Algorithm realizes that search engine keywords optimize, its false code process
Input：The kernel keyword that website is extracted, cluster, random initializtion Subject Matrix J are initialized based on ε fields；
Output：Global similarity functionThe maximum C cluster of summation.
Claims (2)
1. improved Fuzzy C-Means Algorithm realizes that search engine keywords optimize, the present invention relates to Semantic Web technology field,
Specifically related to improved Fuzzy C-Means Algorithm realizes that search engine keywords optimize, it is characterized in that, comprise the following steps：
Step 1：Kernel keyword is determined according to business event, related keyword is collected using search engine, these keywords exist
There are corresponding data items in search engine, such as national monthly volumes of searches, degree of contention and each clicking cost of estimation（CPC）Deng
Step 2：With reference to enterprise product and market analysis, the above-mentioned related keyword set for searching of dimensionality reduction is screened；
Step 3：For the keyword set after screening dimensionality reduction, by the corresponding page of search engine search keyword, remember here
Dimensionality reduction is four-dimensional again by five dimensional vectors for record homepage webpage number and total searched page number, i.e. each keyword, and it was specifically calculated
Journey is as follows：
Here associative key number is m, existing followingMatrix：
、、、、It is followed successively by monthly volumes of searches, degree of contention, the estimation of i-th corresponding this country of keyword
Each clicking cost（CPC）, homepage webpage number, total searched page number dimensionality reduction again
It is the four-dimension, i.e.,
It is search efficiency,It is value rate, as following formula：
Step 4：Improved Fuzzy C-Means Algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is as follows：
Step 4.1：Initialization data object set D is C classes, and Subject Matrix J is initialized with the random number between value [0,1], makes it
The whole constraints that satisfaction is subordinate to
Step 4.2：Initialize eachThe similarity function in field, build C class catalogue scalar functions
Step 4.3：According to decision functionResult, recalculate Ge Cu centers；
Step 4.4：WithNew Subject Matrix J is calculated, then goes to step 4.2, otherwise iteration terminates, export cluster result
Step 5：According to enterprise's concrete condition, comprehensive keyword efficiency optimization and value rate optimize, and select suitable keyword excellent
Change strategy and reach web information flow target.
2. the improved Fuzzy C-Means Algorithm according to claim 1 realizes that search engine keywords optimize, its feature
It is that the specific calculating process in the above step 4 is as follows：
Step 4：Improved Fuzzy C-Means Algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is as follows：
Step 4.1：Initialization data object set D is C classes, and Subject Matrix J is initialized with the random number between value [0,1], makes it
The whole constraints that satisfaction is subordinate to, its specific calculating process is as follows：
According toField initialization data object set D is divided into C classes；
Initializing Subject Matrix J is：
Belong to the degree coefficient of j classes for keyword i, i.e.,、
The whole constraints being subordinate to is：
Step 4.2：Initialize eachThe similarity function in field, build C class catalogue scalar functions, its specific calculating process is as follows：
Above formulaFor eachThe number of data object in field,For eachI-th pass in field
Keyword vectorThe vectorial similarity with its central point,、Respectively quantity, similarity functionInfluence coefficient, and,, its value can according to experiment iteration go out
Suitable value
To belong to the space vector of i-th keyword of j classes,It is j class cluster center vectors, h is vectorial corresponding element
Number
Build C class catalogue scalar functionsFor：
It is the n Lagrange multiplier of constraint formula, to all input parameter derivations, you can must reach above formula
Maximum necessary condition, i.e.,、Value；
Above formulaVector corresponding to keyword i；
Step 4.3：According to decision functionResult, recalculate Ge Cu centers, its specific calculating process is as follows：
The global similarity function that current iteration is obtained,For preceding
The global similarity function that an iteration is obtained, when the iteration result difference for obtaining meets above formula decision condition, then algorithm stops
Step 4.4：WithNew Subject Matrix J is calculated, then goes to step 4.2, otherwise iteration terminates, export cluster result
Concrete structure flow such as Fig. 2 of improved Fuzzy C-Means Algorithm.
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| CN111476303A (en) * | 2020-04-09 | 2020-07-31 | 国网河北省电力有限公司电力科学研究院 | Line loss analysis method of fuzzy C-means clustering based on quantum optimization particle swarm |
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| CN103258000A (en) * | 2013-03-29 | 2013-08-21 | 北界创想（北京）软件有限公司 | Method and device for clustering high-frequency keywords in webpages |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110807099A (en) * | 2019-10-30 | 2020-02-18 | 云南电网有限责任公司信息中心 | Text analysis retrieval method based on fuzzy set |
| CN110807099B (en) * | 2019-10-30 | 2022-05-17 | 云南电网有限责任公司信息中心 | Text analysis retrieval method based on fuzzy set |
| CN111476303A (en) * | 2020-04-09 | 2020-07-31 | 国网河北省电力有限公司电力科学研究院 | Line loss analysis method of fuzzy C-means clustering based on quantum optimization particle swarm |
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