CN106933953A - A kind of fuzzy K mean cluster algorithm realizes search engine optimization technology - Google Patents

A kind of fuzzy K mean cluster algorithm realizes search engine optimization technology Download PDF

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CN106933953A
CN106933953A CN201710046723.9A CN201710046723A CN106933953A CN 106933953 A CN106933953 A CN 106933953A CN 201710046723 A CN201710046723 A CN 201710046723A CN 106933953 A CN106933953 A CN 106933953A
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mean cluster
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金平艳
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Sichuan Yonglian Information Technology Co Ltd
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    • G06F16/95—Retrieval from the web
    • G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
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    • G06F18/23—Clustering techniques
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    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate

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Abstract

A kind of fuzzy K mean cluster algorithm realizes search engine optimization technology, 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 eventDeng, dimension-reduction treatment again is carried out to above-mentioned keyword set, each keyword First Five-Year Plan dimensional vector is represented, increase homepage webpage number and total searched page number, and then the four-dimension, a kind of fuzzy K mean cluster algorithm are reduced to again by five dimensions, initialize the essential attribute between random Subject Matrix, comprehensive two keywordsAnd similarity, according to parameter、、Convergence obtains cluster result, the accuracy of cluster result of the present invention is higher, run time complexity is low, processing speed faster, cluster result is accurate, certain flow can be brought with fast lifting keyword ranking, for enterprise web site, so as to reach preferable web information flow target.

Description

A kind of fuzzy K mean cluster algorithm realizes search engine optimization technology
Technical field
The present invention relates to Semantic Web technology field, and in particular to a kind of fuzzy K mean cluster algorithm realizes search engine Optimisation technique.
Background technology
With internet economy develop rapidly and network deep popularization, search engine have become enterprise rollout from Oneself a kind of critically important stage.Search engine optimization, referred to as popular saying be by website overall architecture, web page contents, Link in keyword and webpage carries out the Optimization Work of correlation, improves its row in Search Results in particular search engine Name, so that website visiting amount is improved, the technology of the final sales force or publicity capacity for lifting website.At present on search engine The theoretical research of optimization method, compared with horn of plenty, is respectively domain name such as black cap technology and white cap technology, search engine optimization strategy Strategy, webpage design planning strategy, keyword strategy and link policy.SEO is the optimisation strategy of keyword after all, is closed Keyword optimisation strategy is broadly divided into two stages：First stage is the extraction of keyword；Second stage is the insertion of keyword.It is existing Modern theoretical research and technology application both at home and abroad to keyword optimization is relatively more, but temporarily does not propose an effective method to simplify Key word analysis flow, also neither one perfect mechanism manage keyword optimisation strategy and progress.Based on the demand, this Invention realizes search engine optimization technology there is provided a kind of fuzzy K mean cluster algorithm.
The content of the invention
The technical problem that search engine optimization is realized in keyword optimization is directed to, the invention provides a kind of fuzzy K averages Clustering algorithm realizes search engine optimization technology.
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：A kind of fuzzy K mean cluster algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is such as Under：
Step 4.1 filters out k clusters using the k-means algorithm initialization clusters based on ε fields；
Step 4.2:Subject Matrix J is initialized with the random number between value [0,1], the whole constraint bar for being subordinate to its satisfaction Part；
Step 4.3：K class catalogue scalar functions J are built, is comprehensively subordinate to constraints, build m equation group, it is asked Solution, you can obtain the necessary condition c for making catalogue scalar functions J maximumj、wij；
Step 4.4：According to decision function wij、cj, Δ (J) size determine the end of iteration；
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, the degree of accuracy of this algorithm classification result more meets empirical value；
7th, the essential attribute and similarity between two keywords have been considered, accuracy is higher, constructs corresponding parameter Model, simplifies cluster process.
Brief description of the drawings
A kind of fuzzy K mean cluster algorithms of Fig. 1 realize search engine optimization technical pattern flow chart
A kind of applicating flow chart of the fuzzy K mean cluster 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
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：A kind of fuzzy K mean cluster algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is such as Under：
Step 4.1：Using the k-means algorithm initialization clusters based on ε fields, k clusters are filtered out；
Step 4.2:Subject Matrix J is initialized with the random number between value [0,1], the whole constraint bar for being subordinate to its satisfaction Part；
Random Subject Matrix J is built for m × k：
wijBelong to the degree coefficient of j classes for keyword i, i.e. (1,2 ... k), (1,2 ... m) for i ∈ for j ∈.
The whole constraints being subordinate to is：
Step 4.3：K class catalogue scalar functions J are built, is comprehensively subordinate to constraints, build m equation group, it is asked Solution, you can obtain the necessary condition c for making catalogue scalar functions J maximumj、wij, its specific calculating process is as follows：
According to catalogue scalar functions J, build with the m Lagrangian equation group of constraint formula, that is, have following formula：
Above formula nεjIt is j class data object numbers,Vector corresponding to j class keywords x,It is j class keywords y institutes Correspondence vector, Δ d is the attribute difference of two keywords in j classes；
Above formula h be data object attribute number, h=4,It is j class keywords xihjAverage, similarly,For j classes are closed Keyword yihjAverage.
λi(i=1 ..., is m) the m Lagrangian of constraint formula, derivation is carried out to above-mentioned formula, to all inputs Parameter derivation, you can try to achieve the necessary condition c for making J reach maximumj、wij：
HereIt is class j clusters center, with aboveIt is different,It is data object correspondence vector.
Step 4.4：According to decision function wij、cj, Δ (J) size determine the end of iteration, its specific calculating process It is as follows：
Δ (J)=Jnew-Jold＜ θ
Δwij＜ μ
Δcj＜ γ
Above formula JnewIt is the general objective functional value of current iteration, JoldIt is last general objective functional value, Δ wijChanged for front and rear In generation, is subordinate to changing value, Δ cjIt is the class center changing value of front and rear iteration, θ, μ, γ are sufficiently small threshold value.Only meet above-mentioned Three conditions, then iteration terminate, export optimal cluster result.
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.

Claims (2)

1. a kind of fuzzy K mean cluster algorithm realizes search engine optimization technology, the present invention relates to Semantic Web technology field, tool Body is related to a kind of fuzzy K mean cluster algorithm to realize search engine optimization technology, 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 estimationDeng
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：
、、、、Monthly volumes of searches, degree of contention, estimation are every to be followed successively by the corresponding this country of i-th keyword Secondary 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：A kind of fuzzy K mean cluster algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is as follows：
Step 4.1：Using being based onThe k-means algorithm initialization clusters in field, filter out k clusters；
Step 4.2:Subject Matrix J is initialized with the random number between value [0,1], the whole constraints for being subordinate to its satisfaction；
Step 4.3：K class catalogue scalar functions J are built, is comprehensively subordinate to constraints, build m equation group, it solved, i.e., The necessary condition for making catalogue scalar functions J maximum can be obtained、；
Step 4.4：According to decision function、、Size determine the end of iteration；
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. a kind of fuzzy K mean cluster algorithm according to claim 1 realizes search engine optimization technology
It is characterized in that, the specific calculating process in the above step 4 is as follows：
Step 4：A kind of fuzzy K mean cluster algorithm, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is as follows：
Step 4.1：Using being based onThe k-means algorithm initialization clusters in field, filter out k clusters；
Step 4.2:Subject Matrix J is initialized with the random number between value [0,1], the whole constraints for being subordinate to its satisfaction；
Building random 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 4.3：K class catalogue scalar functions J are built, is comprehensively subordinate to constraints, build m equation group, it solved, i.e., The necessary condition for making catalogue scalar functions J maximum can be obtained、, its specific calculating process is as follows：
According to catalogue scalar functions J, build with the m Lagrangian equation group of constraint formula, that is, have following formula：
Above formulaIt is j class data object numbers,Vector corresponding to j class keywords x,It is right for j class keywords y Should be vectorial,It is the attribute difference of two keywords in j classes；
Above formula h be data object attribute number, h=4,It is j class keywordsAverage, similarly,It is j class keywordsAverage
It is the m Lagrangian of constraint formula, derivation is carried out to above-mentioned formula, to all input parameter Derivation, you can try to achieve the necessary condition for making J reach maximum、：
HereIt is class j clusters center, with aboveIt is different,It is data object correspondence vector
Step 4.4：According to decision function、、Size determine the end of iteration, its specific calculating process is as follows：
Above formulaIt is the general objective functional value of current iteration,It is last general objective functional value,It is front and rear iteration Be subordinate to changing value,It is the class center changing value of front and rear iteration,、、It is sufficiently small threshold value, only meets above-mentioned three Individual condition, then iteration terminate, export optimal cluster result.
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Cited By (4)

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CN108764991A (en) * 2018-05-22 2018-11-06 江南大学 Information of supply chain analysis method based on K-means algorithms
CN110659930A (en) * 2019-08-27 2020-01-07 深圳大学 Consumption upgrading method and device based on user behaviors, storage medium and equipment
CN110858232A (en) * 2018-08-09 2020-03-03 阿里巴巴集团控股有限公司 Search method, apparatus, system and storage medium
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108764991A (en) * 2018-05-22 2018-11-06 江南大学 Information of supply chain analysis method based on K-means algorithms
CN108764991B (en) * 2018-05-22 2021-11-02 江南大学 Supply chain information analysis method based on K-means algorithm
CN110858232A (en) * 2018-08-09 2020-03-03 阿里巴巴集团控股有限公司 Search method, apparatus, system and storage medium
CN110858232B (en) * 2018-08-09 2024-03-29 阿里巴巴集团控股有限公司 Searching method, searching device, searching system and storage medium
CN110659930A (en) * 2019-08-27 2020-01-07 深圳大学 Consumption upgrading method and device based on user behaviors, storage medium and equipment
US11397754B2 (en) 2020-02-14 2022-07-26 International Business Machines Corporation Context-based keyword grouping

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