CN106897358A - Clustering algorithm based on constraints realizes that search engine keywords optimize - Google Patents
Clustering algorithm based on constraints realizes that search engine keywords optimize Download PDFInfo
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Abstract
Clustering algorithm based on constraints 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 eventDeng, 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, clustering algorithm based on constraints is clustered to above-mentioned keyword, the present invention is higher than traditional clustering method degree of accuracy, part is according to the degree of correlation come the result of partition clustering, more meet empirical value, the overall situation considers the accounting in each field, reduce isolated point influences on cluster result, simplify key word analysis flow simultaneously, data process effects are good, run time complexity is low, processing speed is faster, ranking of the energy fast lifting keyword in website, certain flow can be brought for website, so as to reach preferable web information flow target.
Description
Technical field
The present invention relates to Semantic Web technology field, and in particular to the clustering algorithm based on constraints realizes search engine
Keyword optimizes.
Background technology
Search engine has turned into the important tool that numerous netizens obtain information.Search engine optimization (Search
Engine Optimization, abbreviation SEO) refer to that series of optimum is carried out to website using correlation technique, so as to improve corresponding
Keyword ranking on a search engine, is finally reached the purpose of website marketing.In fact, search engine optimization is exactly to carry out network
A kind of form of marketing, allows enterprise utilizing main search engine optimization strategy, to the keyword in webpage, content and chain
The various factors strategy such as connecing carries out the optimization of correlation so that the enterprise web site after application strategy can be by major main flow search engines
Preferentially capture and include, in the top in target pages are indexed, attraction clicking rate, so as to can reach raising corporate image, push away
The purpose of wide website.In with regard to the form of current all-network marketing, search engine optimization undoubtedly can in a short time expand shadow
Sound, the preferred approach of enterprise web image.SEO is the optimization of keyword after all.Keyword is user in search phase
The word or expression used during the page is closed, is also that search engine is setting up the word that concordance list is used.Contributed to using keyword
Obtain search engine inquiry ranking higher, it should be noted that keyword research is intended to find out the keyword of most worthy.It is domestic at present
Outer theoretical research and technology application to keyword optimization is relatively more, but temporarily does not propose an effective method to simplify keyword
Analysis process, also neither one perfect mechanism manage keyword optimisation strategy and progress.Based on the demand, the present invention is carried
The clustering algorithm based on constraints has been supplied to realize that search engine keywords optimize.
The content of the invention
The technical problem that search engine optimization is realized in keyword optimization is directed to, the invention provides based on constraints reality
Existing search engine keywords optimization.
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：Clustering algorithm based on constraints, 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；
Step 4.2：Initialize the information flow function in each ε fieldFollowing judgements are pressed from set of data objects D
Condition selects k initial cluster center；
Step 4.3：To every class keywords i, (i ∈ (1,2 ..., m)) are redistributed, poly- by probability function p (i) selection
Class center j '；
Step 4.4：According to the result of decision function Δ (I), Ge Cu centers are recalculated；
Step 4.5：If cluster center changes, step 4.2 is gone to, otherwise iteration terminates, export 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.
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 part distinguishes each class from the degree of correlation, and the degree of accuracy of classification results more meets empirical value.
7, the overall situation considers the accounting in the field of each, can so reduce influence of the isolated point to cluster result.
8th, the effect of data processing is more preferable.
Brief description of the drawings
The clustering algorithm that Fig. 1 is based on constraints realizes that search engine keywords optimize structure flow chart
Fig. 2 is based on applicating flow chart of the clustering algorithm of constraints 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：Clustering algorithm based on constraints, clustering processing is carried out to above-mentioned keyword, and its specific sub-step is such as
Under：
Step 4.1：It is c clusters using the k-means algorithm initializations based on ε fields.
Step 4.2：With the number initialization Subject Matrix J between value [0,1], it is set to meet the whole constraints being subordinate to, its
Specific calculating process is as follows：
Above formulaI-th crucial term vector and cluster center vector in for spaceInner product, μijFor keyword i is subordinate to
Belong to the degree coefficient of class j, it meets and following is subordinate to constraints：
Initialization Subject Matrix J is m × c：
Step 4.3：Initialize each field object functionC class catalogue scalar functions are built, is comprehensively subordinate to constraint bar
Part, builds m equation group, and it is solved, you can obtain cluster result, and its specific calculating process is as follows：
Above formula nεjIt is the number of data object in j class ε fields.
C class catalogue scalar functions are
Comprehensively it is subordinate to constraints, builds m equation group：
Here λi(i ∈ (1,2 ..., m)) are the m Lagrange multipliers of constraint formula.Parameter derivations are input into all, i.e.,
Can try to achieve makesReach the necessary condition c of maximumj、μij；
Above formula xiVector corresponding to keyword i；
Step 4.4：Using the result of following formula decision function Δ (I), Ge Cu centers are recalculated, its specific calculating process is such as
Under：
Decision function Δ (I)：
Above formulaIt is new catalogue scalar functions,For the catalogue scalar functions that last iteration draws.θ is a foot
Enough small numbers, only meet above-mentioned condition, then have found optimal classification, do not find otherwise.
Concrete structure flow such as Fig. 2 of clustering algorithm based on constraints.
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.
Clustering algorithm based on constraints realizes that search engine keywords optimize, its false code process
Input：The kernel keyword that website is extracted, c classes are initialized as based on ε fields
Output：Global catalogue scalar functionsThe maximum c cluster of summation.
Claims (2)
1. the clustering algorithm based on constraints realizes that search engine keywords optimize, the present invention relates to Semantic Web technology neck
Domain, and in particular to the clustering algorithm based on constraints realizes that search engine keywords optimize, it is characterized in that, including following step
Suddenly：
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：Clustering algorithm based on constraints, 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；
Step 4.2：Initialize eachThe information flow function in field, following judgements are pressed from set of data objects D
Condition selects k initial cluster center；
Step 4.3：To every class keywordsRedistributed, by probability functionSelection is poly-
Class center；
Step 4.4：According to decision functionResult, recalculate Ge Cu centers；
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 clustering algorithm based on constraints according to claim 1 realizes that search engine keywords optimize, and it is special
Levying is, the specific calculating process in the above step 4 is as follows：
Step 4：Clustering algorithm based on constraints, 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 initializations in field are c clusters
Step 4.2：With the number initialization Subject Matrix J between value [0,1], the whole constraints for being subordinate to its satisfaction, its is specific
Calculating process is as follows：
Above formulaI-th crucial term vector and cluster center vector in for spaceInner product,For keyword i is subordinate to
Belong to the degree coefficient of class j, it meets and following is subordinate to constraints：
Initializing Subject Matrix J is：
Step 4.3：Initialize each field object function, c class catalogue scalar functions are built, comprehensively it is subordinate to constraint bar
Part, builds m equation group of Ah, and it is solved, you can obtain cluster result, and its specific calculating process is as follows：
Above formulaIt is j classesThe number of data object in field
C class catalogue scalar functions are：
Comprehensively it is subordinate to constraints, builds m equation group：
HereIt is the m Lagrange multiplier of constraint formula, to all input parameter derivations, you can ask
Must makeReach the necessary condition of maximum、；
Above formulaVector corresponding to keyword i；
Step 4.4：Using following formula decision functionResult, recalculate Ge Cu centers, its specific calculating process is as follows：
Decision function：
Above formulaIt is new catalogue scalar functions,It is the catalogue scalar functions that last iteration draws,It is a foot
Enough small numbers, only meet above-mentioned condition, then have found optimal classification, do not find otherwise.
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| CN103218435A (en) * | 2013-04-15 | 2013-07-24 | 上海嘉之道企业管理咨询有限公司 | Method and system for clustering Chinese text data |
| 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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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN103218435A (en) * | 2013-04-15 | 2013-07-24 | 上海嘉之道企业管理咨询有限公司 | Method and system for clustering Chinese text data |
Non-Patent Citations (2)
| Title |
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| 林元国 等: "K-means算法在关键词优化中的应用", 《计算机***应用》 * |
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