CN106776915A - A kind of new clustering algorithm realizes that search engine keywords optimize - Google Patents

A kind of new clustering algorithm realizes that search engine keywords optimize Download PDF

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Publication number
CN106776915A
CN106776915A CN201611086516.8A CN201611086516A CN106776915A CN 106776915 A CN106776915 A CN 106776915A CN 201611086516 A CN201611086516 A CN 201611086516A CN 106776915 A CN106776915 A CN 106776915A
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keyword
search engine
cluster
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clustering algorithm
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金平艳
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Sichuan Yonglian Information Technology Co Ltd
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Sichuan Yonglian Information Technology Co Ltd
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    • 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

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Abstract

A kind of new clustering 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 eventDeng, dimension-reduction treatment again is carried out to above-mentioned keyword set, each keyword First Five-Year Plan dimensional vector is represented, that is, increase homepage webpage number and total searched page number, and then is reduced to the four-dimension again by five dimensions, finally using a kind of new clustering algorithm to keyword clustering, eachThe information flow function in field isInventive algorithm is more simple and effective, run time complexity is low, processing speed is faster, classification results more meet empirical value, with more preferable data process effects, can help the ranking of website its keyword of fast lifting in a short time, for enterprise web site brings certain flow and inquiry, so as to reach preferable web information flow target.

Description

A kind of new clustering algorithm realizes that search engine keywords optimize
Technical field
The present invention relates to Semantic Web technology field, and in particular to a kind of new clustering algorithm method realizes that search engine is crucial Word optimizes.
Background technology
Index is held up 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.Keyword is The word or expression that user uses when related pages are searched for, is also that search engine is setting up the word that concordance list is used.Utilize Keyword helps to obtain search engine inquiry ranking higher, it should be noted that keyword research is intended to find out the key of most worthy Word.Search engine optimization technology includes black cap technology and white cap technology, wherein black cap technology represents violation search engine optimization rule Malice optimisation technique then, show as being piled up in the page keyword in keyword optimisation technique or place unrelated keyword with Ranking in a search engine is improved, current each search engine has been incorporated into correlation technique and rule to the net using black cap technology Station is punished；White cap technology then represents the optimisation technique of searched engine accreditation.At present both at home and abroad to the reason of keyword optimization It is relatively more by research and technology application, but temporarily do not propose an effective method to simplify key word analysis flow, also without one Individual perfect mechanism manages keyword optimisation strategy and progress.Based on the demand, the invention provides a kind of new cluster Algorithm algorithm realizes that search engine keywords are excellent.
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 new cluster calculation Method 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 a kind of new clustering algorithm, clustering processing is carried out to above-mentioned keyword, its specific sub-step is as follows：
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, the degree of accuracy of this algorithm classification result more meets empirical value.
7th, this algorithm is more simple and effective.
8th, the effect of data processing is more preferable.
Brief description of the drawings
A kind of new clustering algorithms of Fig. 1 realize that search engine keywords optimize structure flow chart
A kind of applicating flow chart of the new clustering 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 a kind of new clustering algorithm, clustering processing is carried out to above-mentioned keyword, its specific sub-step is as follows：
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, and its specific calculating process is as follows：
Above formula nεIt is the number of data object in each ε field,I-th in for space crucial term vector and its Cluster center vectorInner product.
Decision condition is as follows：
γ is the threshold value for setting, and only meets above formula condition and is then classified as cluster, then screen k classes out.
Step 4.3：To every class keywords i, (i ∈ (1,2 ..., m)) are redistributed, poly- by probability function p (i) selection Class center j ', its specific calculating process is as follows：
By the corresponding cluster centre j ' of p (i) value MAXIMUM SELECTIONs.
Step 4.4：According to the result of decision function Δ (I), Ge Cu centers are recalculated, its specific calculating process is as follows：
Meet above formula, then recalculate Ge Cu centers.
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.
A kind of new clustering algorithm realizes that search engine keywords optimize, its false code process
Input：The kernel keyword that website is extracted, cluster is initialized based on ε fields, initializes the information content in each ε field Function
Output：Global information flow function Ii→jThe maximum k cluster of summation.

Claims (2)

1. a kind of new clustering algorithm realizes that search engine keywords optimize, the present invention relates to Semantic Web technology field, specifically It is related to a kind of new clustering algorithm method 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 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：
、、、、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, homepage webpage number, total searched page number
Dimensionality reduction is the four-dimension again, i.e.,
It is search efficiency,It is value rate, as following formula：
Step 4：Using a kind of new clustering algorithm, 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 initialization clusters in field；
Step 4.2：Initialize eachThe information flow function in field, following judgement bars are pressed from set of data objects D Part selects k initial cluster center；
Step 4.3：To every class keywordsRedistributed, by probability functionIn selection cluster The heart；
Step 4.4：According to decision functionResult, recalculate Ge Cu centers；
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 select suitable keyword excellent Change strategy and reach web information flow target.
2. a kind of new clustering algorithm according to claim 1 realizes 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 a kind of new clustering algorithm, 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 initialization clusters in field
Step 4.2：Initialize eachThe information flow function in field, following judgement bars are pressed from set of data objects D Part selects k initial cluster center, and its specific calculating process is as follows：
Above formulaFor eachThe number of data object in field,I-th crucial term vector and its cluster in for space Center vectorInner product
Decision condition is as follows：
It is the threshold value for setting, only meets above formula condition and be then classified as cluster, then screens k classes out
Step 4.3：To every class keywordsRedistributed, by probability functionIn selection cluster The heart, its specific calculating process is as follows：
PressThe corresponding cluster centre of value MAXIMUM SELECTION
Step 4.4：According to decision functionResult, recalculate Ge Cu centers, its specific calculating process is as follows：
Meet above formula, then recalculate Ge Cu centers
Step 4.5：If cluster center changes, step 4.2 is gone to, otherwise iteration terminates, export cluster result.
CN201611086516.8A 2016-11-30 2016-11-30 A kind of new clustering algorithm realizes that search engine keywords optimize Pending CN106776915A (en)

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Cited By (1)

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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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CN110059725B (en) * 2019-03-21 2021-07-09 中国科学院计算技术研究所 Malicious search detection system and method based on search keywords

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