CN106897377A - Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies - Google Patents

Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies Download PDF

Info

Publication number
CN106897377A
CN106897377A CN201710040713.4A CN201710040713A CN106897377A CN 106897377 A CN106897377 A CN 106897377A CN 201710040713 A CN201710040713 A CN 201710040713A CN 106897377 A CN106897377 A CN 106897377A
Authority
CN
China
Prior art keywords
keyword
fuzzy
clustering algorithm
means clustering
global position
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201710040713.4A
Other languages
Chinese (zh)
Inventor
金平艳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sichuan Yonglian Information Technology Co Ltd
Original Assignee
Sichuan Yonglian Information Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sichuan Yonglian Information Technology Co Ltd filed Critical Sichuan Yonglian Information Technology Co Ltd
Priority to CN201710040713.4A priority Critical patent/CN106897377A/en
Publication of CN106897377A publication Critical patent/CN106897377A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Probability & Statistics with Applications (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies, 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, Fuzzy c-Means Clustering Algorithm based on global position is to above-mentioned keyword clustering, further according to enterprise's concrete condition, selection is adapted to the keyword optimisation strategy of oneself, it is as a reference point that the present invention set up a global optimum position, the result for obtaining is more accurate, data process effects are good, reduce human error, with reference to Fuzzy c-Means Clustering Algorithm, avoid cluster Premature Convergence, reduce whole web information flow workload simultaneously, run time complexity is low, processing speed is faster, can be with fast lifting keyword ranking.

Description

Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies
Technical field
The present invention relates to Semantic Web technology field, and in particular to a kind of Fuzzy c-means Clustering based on global position is calculated Method realizes SEO technologies.
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.SEO is the optimization of keyword after all.Close Keyword optimisation strategy is broadly divided into two stages:First stage is the extraction of keyword；Second stage is the insertion of keyword.Though Right search engine optimization is theoretical very ripe, but is adapted to the search engine optimization of enterprise web site also even starting stage, right In keyword selection mostly by virtue of experience and subjective factor, also the perfect mechanism of neither one manages keyword optimisation strategy And progress.To make the selection more scientific and objectivity of keyword, based on the demand, the invention provides based on global position The Fuzzy c-Means Clustering Algorithm put realizes SEO technologies.
The content of the invention
The technical problem that search engine optimization is realized in keyword optimization is directed to, the invention provides one kind based on global position The Fuzzy c-Means Clustering Algorithm put realizes SEO technologies.
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：Fuzzy c-Means Clustering Algorithm based on global position, clustering processing is carried out to above-mentioned keyword, and its is specific Sub-step is as follows：
Step 4.1：It is c classes using the k-means algorithm initializations 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：Initialize each field object functionBuild c class catalogue scalar functionsComprehensively it is subordinate to Constraints, builds m equation group, and it is solved, you can obtain cluster result；
Step 4.4：Using the result of following formula decision function Δ (f), Ge Cu centers are recalculated；
Step 4.5：If cluster center changes, step 4.2 is gone to, recalculate Subject Matrix J, otherwise iteration knot Beam, exports 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 set up that a global optimum position is as a reference point, and the result precision for obtaining is higher；
7th, this algorithm has carried out data processing, and the result for obtaining can reduce human error；
8th, with reference to Fuzzy c-Means Clustering Algorithm, it is to avoid cluster Premature Convergence.
Brief description of the drawings
Fuzzy c-Means Clustering Algorithms of the Fig. 1 based on global position realizes the structure flow chart of SEO technologies
Fig. 2 is based on applicating flow chart of the Fuzzy c-Means Clustering Algorithm of global position 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：Fuzzy c-Means Clustering Algorithm based on global position, clustering processing is carried out to above-mentioned keyword, and its is specific Sub-step is as follows：
Step 4.1：It is c classes using the k-means algorithm initializations 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；
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 4.3：Initialize each field object functionBuild c class catalogue scalar functionsComprehensively it is subordinate to Constraints, builds m equation group, and it is solved, you can obtain cluster result, and its specific calculating process is as follows：
Build c class catalogue scalar functions
Above formula d (i, wq) it is keyword i to overall situation optimum position wqDistance, d (i, wj) it is keyword i to cluster centre j Distance, α is smooth coefficients.
Above formula xirWith yjr4 dimensional vectors of respectively keyword i, cluster centre object j, i.e. xir∈(Xi, Zi, NiS, NiY)、 yjr∈(Xj, Zj, NjS, NjY)。
Comprehensively it is subordinate to constraints, builds m equation group：
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 trying to achieve makesReach the necessary condition c of maximumj、wij：
Above formulaVector corresponding to keyword i；
Step 4.4：Using the result of following formula decision function Δ (f), Ge Cu centers are recalculated, its specific calculating process is such as Under：
It is new catalogue scalar functions,It is the catalogue scalar functions that last iteration draws, θ is one sufficiently small Number, only meet above-mentioned condition, then have found optimal classification.
Step 4.5：If cluster center changes, step 4.2 is gone to, recalculate Subject Matrix J, otherwise iteration knot Beam, exports cluster result.
Fuzzy c-Means Clustering Algorithm concrete structure flow such as Fig. 2 based on global position.
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.
Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies, its false code process
Input：The kernel keyword that website is extracted, initializes the number c of cluster
Output：High-quality keyword after series of optimum.

Claims (2)

1. the Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies, the present invention relates to Semantic Web technology field, Specifically related to the Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies, 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：Fuzzy c-Means Clustering Algorithm based on global position, clustering processing is carried out to above-mentioned keyword, its specific sub-step It is rapid as follows：
Step 4.1：Using being based onThe k-means algorithm initializations in field are c classes
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：Initialize each field object function, build c class catalogue scalar functions, comprehensively it is subordinate to Constraints, builds m equation group, and it is solved, you can obtain cluster result；
Step 4.4：Using following formula decision functionResult, recalculate Ge Cu centers；
Step 4.5：If cluster center changes, step 4.2 is gone to, recalculates Subject Matrix J, otherwise iteration terminates, Output 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 Fuzzy c-Means Clustering Algorithm based on global position according to claim 1 realizes SEO technologies,
It is characterized in that, the specific calculating process in the above step 4 is as follows：
Step 4：Fuzzy c-Means Clustering Algorithm based on global position, clustering processing is carried out to above-mentioned keyword, its specific sub-step It is rapid as follows：
Step 4.1：Using being based onThe k-means algorithm initializations in field are c classes
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；
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 4.3：Initialize each field object function, build c class catalogue scalar functions, comprehensively it is subordinate to Constraints, builds m equation group, and it is solved, you can obtain cluster result, and its specific calculating process is as follows：
Build c class catalogue scalar functions：
Above formulaIt is keyword i to global optimum positionDistance,For in keyword i to cluster The distance of heart j,It is smooth coefficients
Above formulaWith4 dimensional vectors of respectively keyword i, cluster centre object j, i.e.,、
Comprehensively it is subordinate to constraints, builds m equation group：
It is the m Lagrangian of constraint formula, derivation is carried out to above-mentioned formula, to all input Parameter derivation, you can trying to achieve makesReach 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：
It is new catalogue scalar functions,It is the catalogue scalar functions that last iteration draws,For one it is sufficiently small Number, only meet above-mentioned condition, then have found optimal classification
Step 4.5：If cluster center changes, step 4.2 is gone to, recalculates Subject Matrix J, otherwise iteration terminates, Output cluster result
Fuzzy c-Means Clustering Algorithm concrete structure flow such as Fig. 2 based on global position.
CN201710040713.4A 2017-01-19 2017-01-19 Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies Pending CN106897377A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710040713.4A CN106897377A (en) 2017-01-19 2017-01-19 Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710040713.4A CN106897377A (en) 2017-01-19 2017-01-19 Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies

Publications (1)

Publication Number Publication Date
CN106897377A true CN106897377A (en) 2017-06-27

Family

ID=59198946

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710040713.4A Pending CN106897377A (en) 2017-01-19 2017-01-19 Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies

Country Status (1)

Country Link
CN (1) CN106897377A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Patent Citations (2)

* Cited by examiner, † Cited by third party
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)

* Cited by examiner, † Cited by third party
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》 *

Similar Documents

Publication Publication Date Title
CN111881342A (en) Recommendation method based on graph twin network
CN111881363B (en) Recommendation method based on graph interaction network
EP3300002A1 (en) Method for determining the similarity of digital images
Liu et al. Real-time social recommendation based on graph embedding and temporal context
CN108563690A (en) A kind of collaborative filtering recommending method based on object-oriented cluster
Sarwar et al. A survey of big data analytics in healthcare
JP7427717B2 (en) Multimodal transformer-based item classification system, data processing system, data processing method, and computer-implemented method
CN106649616A (en) Clustering algorithm achieving search engine keyword optimization
CN106933954A (en) Search engine optimization technology is realized based on Decision Tree Algorithm
Zhao et al. Heterogeneous star graph attention network for product attributes prediction
CN111949885A (en) Personalized recommendation method for scenic spots
CN106933953A (en) A kind of fuzzy K mean cluster algorithm realizes search engine optimization technology
Diallo et al. Auto-attention mechanism for multi-view deep embedding clustering
CN106909626A (en) Improved Decision Tree Algorithm realizes search engine optimization technology
Liu et al. Community detection based on community perspective and graph convolutional network
CN106874376A (en) A kind of method of verification search engine keyword optimisation technique
Liu et al. Classification of fashion article images based on improved random forest and VGG-IE algorithm
CN107622071A (en) By indirect correlation feedback without clothes image searching system and the method looked under source
Hu et al. WSHE: User feedback-based weighted signed heterogeneous information network embedding
Lv et al. DSMN: An improved recommendation model for capturing the multiplicity and dynamics of consumer interests
CN106897356A (en) Improved Fuzzy C mean algorithm realizes that search engine keywords optimize
CN106802945A (en) Fuzzy c-Means Clustering Algorithm based on VSM realizes that search engine keywords optimize
CN111126467A (en) Remote sensing image space spectrum clustering method based on multi-target sine and cosine algorithm
CN106897377A (en) Fuzzy c-Means Clustering Algorithm based on global position realizes SEO technologies
CN106874377A (en) The improved clustering algorithm based on constraints realizes that search engine keywords optimize

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20170627

WD01 Invention patent application deemed withdrawn after publication