CN106777317A - Improved c mean algorithms realize that search engine keywords optimize - Google Patents
Improved c mean algorithms realize that search engine keywords optimize Download PDFInfo
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- CN106777317A CN106777317A CN201710003652.4A CN201710003652A CN106777317A CN 106777317 A CN106777317 A CN 106777317A CN 201710003652 A CN201710003652 A CN 201710003652A CN 106777317 A CN106777317 A CN 106777317A
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Abstract
Improved c mean algorithms realize 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, using c mean algorithms are improved, calculate Subject Matrix J, structure is subordinate to constraints, comprehensive c classes catalogue scalar functionsComposition one draws and meets the maximum necessary condition of catalogue scalar functions with the m equation group of Lagrange multiplierIf meeting decision condition, then optimal cluster result is exported, do not find otherwise, present invention, avoiding cluster result Premature Convergence, data SNR is improved, run time complexity is low, can be with fast lifting keyword ranking, with bigger value.
Description
Technical field
The present invention relates to Semantic Web technology field, and in particular to a kind of improved c- mean algorithms realize that search engine is closed
Keyword optimizes.
Background technology
In recent years, the support energetically with country to Internet industry, network speed is substantially improved, and network rate decline, knowledge
Economy and informatization fast development, the network information are presented explosive growth, and the mankind have progressed into the big data epoch.It is numerous
The network information enriches the information source of people, also gives people quick obtaining information and causes puzzlement.Search engine precision, people
The information retrieval service of property is approved by numerous users.User improves to the utilization rate of search engine so that search engine is fast
Speed development.
Many researchs at present find, search engine user is general only to pay close attention to net in the top in result of page searching
Stand, the clicking rate of these websites is also relatively higher.Therefore, it is positive enterprise to improve ranking of the website in keyword search results
Thinking, many enterprises are to improve ranking of the website in search engine search results and obtain visit capacity actively to scan for engine
Marketing.Search engine optimization (Search Engine Optimization, abbreviation SEO) refers to that website is entered using correlation technique
Row series of optimum, so as to improve corresponding keyword ranking on a search engine, is finally reached the purpose of website marketing, SEO
After all it is the optimization of keyword.But for keyword selection mostly by virtue of experience and subjective factor, also neither one
Perfect mechanism manages keyword optimisation strategy and progress.To make the selection more scientific and objectivity of keyword, it is based on
The demand, realizes that search engine keywords optimize the invention provides improved c- mean algorithms.
The content of the invention
The technical problem that search engine optimization is realized in keyword optimization is directed to, it is equal the invention provides a kind of improved 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：Using improved c- mean algorithms, clustering processing is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：It is c classes using the k-means algorithm initializations cluster based on ε fields.
Step 4.2：With the number initialization Subject Matrix J between value [0,1], the whole constraints for being subordinate to its satisfaction.
Step 4.3：Initialize each field object function L (S2)start, build c class catalogue scalar functions.
Step 4.4：Using following formula decision function Δ (S2) judge the above results accuracy.
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, cluster result Premature Convergence is avoided；
7th, influence of the isolated point to cluster result is reduced, the signal to noise ratio of data result is improved.
Brief description of the drawings
The improved c- mean algorithms of Fig. 1 realize that search engine keywords optimize structure flow chart
Applicating flow chart of the improved c- mean 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：Using improved c- mean algorithms, clustering processing is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：It is c classes using the k-means algorithm initializations cluster 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 formula wijBelong to the degree coefficient of j classes for keyword i, i.e. j ∈ (1,2 ..., c), i ∈ (1,2 ..., m).dijFor
The distance at keyword i to j classes center.
Initialization Subject Matrix J is m × c：
The whole constraints being subordinate to is：
Step 4.3：Initialize each field object function L (S2)start, build c class catalogue scalar functions, its specific calculating
Process is as follows：
Above formula NεjIt is the number of data object in j class ε fields, xihVector corresponding to data object in j class ε fields,
yihIt is corresponding cluster centre data object vectors in j class ε fields.
Build c class catalogue scalar functions L (S2∑j∈cFor：
Comprehensive constraint condition, constructs following fresh target function, and can try to achieve makes L (S2)∑j∈cReach the necessary condition of maximum：
Above formula λi(i=1,2 ..., m) be the whole constraints being subordinate to Lagrange multiplier.To all parameter derivations,
Make formula reach maximum necessary condition be：
Above formulaVector corresponding to keyword i；
Step 4.4：Using following formula decision function Δ (S2) judge the above results accuracy, its specific calculating process is such as
Under：
Decision function Δ (S2)：
Δ(S2)=L (S2)new ∑j∈c-L(S2)old ∑j∈c＜ θ
Above formula L (S2)new ∑j∈cIt is new catalogue scalar functions, L (S2)old ∑j∈cFor the catalogue offer of tender that last iteration draws
Number.θ is a sufficiently small number, only meets above-mentioned condition, then have found optimal classification, is not found otherwise.
Concrete structure flow such as Fig. 2 of improved C- mean algorithms.
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 c- mean algorithms realize that search engine keywords optimize, its false code process
Input：The kernel keyword that website is extracted, c classes are initialized based on ε fields
Output：High-quality keyword after series of optimum.
Claims (2)
1. improved c- mean algorithms realize that search engine keywords optimize, the present invention relates to Semantic Web technology field, specifically
It is related to improved c- mean algorithms to realize 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：Using improved c- mean algorithms, clustering processing is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：Using being based onThe k-means algorithm initializations cluster in field is c classes
Step 4.2：With the number initialization Subject Matrix J between value [0,1], the whole constraints for being subordinate to its satisfaction
Step 4.3：Initialize each field object function, build c class catalogue scalar functions
Step 4.4：Using following formula decision functionJudge the accuracy of the above results
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 C- mean algorithms according to claim 1 realize that search engine keywords optimize, it is characterized in that, with
Specific calculating process in the upper step 4 is as follows：
Step 4：Using improved c- mean algorithms, clustering processing is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：Using being based onThe k-means algorithm initializations cluster in field is c classes
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 formulaBelong to the degree coefficient of j classes for keyword i, i.e.,、
It is the distance at keyword i to j classes center
Initializing Subject Matrix J is：
The whole constraints being subordinate to is：
Step 4.3：Initialize each field object function, build c class catalogue scalar functions, its specific calculating
Process is as follows：
Above formulaIt is j classesThe number of data object in field,It is j classesIn field corresponding to data object to
Amount,It is j classesCorresponding cluster centre data object vectors in field
Build c class catalogue scalar functionsFor：
Comprehensive constraint condition, constructs following fresh target function, and can try to achieve makesReach the necessary condition of maximum：
Above formulaIt is the Lagrange multiplier of the whole constraints being subordinate to, to all parameter derivations, makes
Formula reaches maximum necessary condition：
Above formulaVector corresponding to keyword i；
Step 4.4：Using following formula decision functionJudge the accuracy of the above results, its specific calculating process is as follows：
Decision function：
Above formulaIt is new catalogue scalar functions,For the catalogue offer of tender that last iteration draws
Number,It is a sufficiently small number, only meets above-mentioned condition, then have found optimal classification, does not find otherwise
Concrete structure flow such as Fig. 2 of improved C- mean algorithms.
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Citations (2)
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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 |
|---|
| 林元国 等: "K-means算法在关键词优化中的应用", 《计算机***应用》 * |
| 邓健爽 等: "基于搜索引擎的关键词自动聚类法", 《计算机科学》 * |
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