CN109271466A - A kind of weather data analysis method based on hierarchical clustering Yu K mean algorithm - Google Patents
A kind of weather data analysis method based on hierarchical clustering Yu K mean algorithm Download PDFInfo
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Abstract
The weather data analysis method based on hierarchical clustering Yu K mean algorithm that the present invention relates to a kind of, belongs to weather data analysis method and technology field.Regional meteorological measuring is collected first generates meteorogical phenomena database；Then: obtaining the data in meteorogical phenomena database, carry out processing analysis using K mean algorithm, and analysis result is stored into result database 1；The data in meteorogical phenomena database are obtained simultaneously, carry out processing analysis using hierarchical clustering algorithm, and analysis result is stored into result database 2；Again by obtaining the data in result database 1 and result database 2, the data in result database are analyzed using result verification method；Suitable climatic analysis prediction data is finally obtained based on the analysis results.The present invention is handled meteorological data using hierarchical clustering algorithm and K mean algorithm, and obtains suitably analyzing prediction result to promote the accuracy of weather data analysis prediction result by result verification method.
Description
Technical field
The weather data analysis method based on hierarchical clustering Yu K mean algorithm that the present invention relates to a kind of, belongs to meteorological data
Analysis method technical field.
Background technique
As data mining and its continuous development of application, data mining are widely used in every field.It utilizes
Ground instrument and remote sensing observations are to meteorological data related with atmospheric environment, the prediction knot obtained by the processing to these data
Fruit all generates positive effect to the decision of many industries.But existing some weather data analysis prediction techniques suffer from standard
The not high disadvantage of true property.
Summary of the invention
The technical problem to be solved by the present invention is to for overcome the deficiencies in the prior art, provide it is a kind of based on hierarchical clustering with
The weather data analysis method of K mean algorithm, to solve the above problems.
The technical scheme is that a kind of weather data analysis method based on hierarchical clustering Yu K mean algorithm, specifically
Step are as follows:
Step1: it collects regional meteorological measuring and generates meteorogical phenomena database；
Step2: obtaining the data in meteorogical phenomena database, carries out processing analysis using K mean algorithm, and analysis result is deposited
It stores up in result database 1；
Step3: obtaining the data in meteorogical phenomena database, carries out processing analysis using hierarchical clustering algorithm, and analysis is tied
Fruit stores into result database 2；
Step4: the data in result database 1 and result database 2 are obtained, using result verification method to result data
Data in library are analyzed；
Step5: suitable climatic analysis prediction data is obtained based on the analysis results.
In the step Step2, K mean algorithm is comprised the concrete steps that:
S1: K mass center of selection；
S2: each meteorological data point is assigned on its immediate mass center；
S3: recalculating mass center, and new mass center is the average value of all meteorological data points；
S4: data point is distributed into their immediate mass centers；
S5: continue step S3 and S4, until maximum number of iterations is not redistributed or reached to observed value.
The hierarchical clustering algorithm comprises the concrete steps that:
S1: each meteorological data point is distributed to the start cluster of oneself, and distributes n cluster for n object；
S2: the distance between all pairs of clusters are calculated using full concatenation measurement to create distance matrix；
S3: the smallest two clusters of distance between them are found out；
S4: the smallest cluster of the spacing found is merged.
S5: step S3 and S4 is constantly repeated when an only remaining cluster, stops algorithm.
In the step S2, the calculation formula of full concatenation measurement are as follows:
Wherein, X and Y represent cluster X and cluster Y, distance of the D (X, Y) between two clusters.
In the step Step4, result verification method are as follows: using profile width and Dunne's index come to both algorithms point
The effect of analysis is verified, and the weather prognosis data that profile width and the biggish algorithm of Dunne's index obtain are more accurate.
The calculation of the profile width is
Wherein, aiIt is the average distance in i and same cluster between every other observation, biIt is that i and nearest neighbor cluster
Average distance between middle observation, biCalculation formula are as follows:
Wherein, C (i) is the cluster comprising observation data i, and dist (i, j) is the distance between observation i and j, and n (C) is
The radix of cluster C.
The calculation of Dunne's index are as follows:
Wherein, diam (Cm) it is cluster CmMaximum distance between middle observation.
The beneficial effects of the present invention are: the present invention carries out meteorological data using hierarchical clustering algorithm and K mean algorithm
Processing, and obtain suitably analyzing prediction result to promote the standard of weather data analysis prediction result by result verification method
True property.
Detailed description of the invention
Fig. 1 is flow chart of steps of the present invention.
Specific embodiment
With reference to the accompanying drawings and detailed description, the invention will be further described.
Embodiment 1: as shown in Figure 1, a kind of weather data analysis method based on hierarchical clustering Yu K mean algorithm, specifically
Step are as follows:
Step1: it collects regional meteorological measuring and generates meteorogical phenomena database；
Step2: obtaining the data in meteorogical phenomena database, carries out processing analysis using K mean algorithm, and analysis result is deposited
It stores up in result database 1；
Step3: obtaining the data in meteorogical phenomena database, carries out processing analysis using hierarchical clustering algorithm, and analysis is tied
Fruit stores into result database 2；
Step4: the data in result database 1 and result database 2 are obtained, using result verification method to result data
Data in library are analyzed；
Step5: suitable climatic analysis prediction data is obtained based on the analysis results.
In the step Step2, K mean algorithm is comprised the concrete steps that:
S1: K mass center of selection；
S2: each meteorological data point is assigned on its immediate mass center；
S3: recalculating mass center, and new mass center is the average value of all meteorological data points；
S4: data point is distributed into their immediate mass centers；
S5: continue step S3 and S4, until maximum number of iterations is not redistributed or reached to observed value.
The hierarchical clustering algorithm comprises the concrete steps that:
S1: each meteorological data point is distributed to the start cluster of oneself, and distributes n cluster for n object；
S2: the distance between all pairs of clusters are calculated using full concatenation measurement to create distance matrix；
S3: the smallest two clusters of distance between them are found out；
S4: the smallest cluster of the spacing found is merged.
S5: step S3 and S4 is constantly repeated when an only remaining cluster, stops algorithm.
In the step S2, the calculation formula of full concatenation measurement are as follows:
Wherein, X and Y represent cluster X and cluster Y, distance of the D (X, Y) between two clusters.
In the step Step4, result verification method are as follows: using profile width and Dunne's index come to both algorithms point
The effect of analysis is verified, and the weather prognosis data that profile width and the biggish algorithm of Dunne's index obtain are more accurate.
The calculation of the profile width is
Wherein, aiIt is the average distance in i and same cluster between every other observation, biIt is that i and nearest neighbor cluster
Average distance between middle observation, biCalculation formula are as follows:
Wherein, C (i) is the cluster comprising observation data i, and dist (i, j) is the distance between observation i and j, and n (C) is
The radix of cluster C.
The calculation of Dunne's index are as follows:
Wherein, diam (Cm) it is cluster CmMaximum distance between middle observation.
In conjunction with attached drawing, the embodiment of the present invention is explained in detail above, but the present invention is not limited to above-mentioned
Embodiment within the knowledge of a person skilled in the art can also be before not departing from present inventive concept
Put that various changes can be made.
Claims (7)
1. a kind of weather data analysis method based on hierarchical clustering Yu K mean algorithm, it is characterised in that:
Step1: it collects regional meteorological measuring and generates meteorogical phenomena database；
Step2: obtaining the data in meteorogical phenomena database, carries out processing analysis using K mean algorithm, and analysis result storage is arrived
In result database 1；
Step3: obtaining the data in meteorogical phenomena database, carries out processing analysis using hierarchical clustering algorithm, and analysis result is deposited
It stores up in result database 2；
Step4: the data in result database 1 and result database 2 are obtained, using result verification method in result database
Data analyzed；
Step5: suitable climatic analysis prediction data is obtained based on the analysis results.
2. the weather data analysis method according to claim 1 based on hierarchical clustering Yu K mean algorithm, feature exist
In: in the step Step2, K mean algorithm is comprised the concrete steps that:
S1: K mass center of selection；
S2: each meteorological data point is assigned on its immediate mass center；
S3: recalculating mass center, and new mass center is the average value of all meteorological data points；
S4: data point is distributed into their immediate mass centers；
S5: continue step S3 and S4, until maximum number of iterations is not redistributed or reached to observed value.
3. the weather data analysis method according to claim 1 based on hierarchical clustering Yu K mean algorithm, feature exist
In: the hierarchical clustering algorithm comprises the concrete steps that:
S1: each meteorological data point is distributed to the start cluster of oneself, and distributes n cluster for n object；
S2: the distance between all pairs of clusters are calculated using full concatenation measurement to create distance matrix；
S3: the smallest two clusters of distance between them are found out；
S4: the smallest cluster of the spacing found is merged.
S5: step S3 and S4 is constantly repeated when an only remaining cluster, stops algorithm.
4. the weather data analysis method according to claim 1 based on hierarchical clustering Yu K mean algorithm, feature exist
In: in the step S2, the calculation formula of full concatenation measurement are as follows:
Wherein, X and Y represent cluster X and cluster Y, distance of the D (X, Y) between two clusters.
5. the weather data analysis method according to claim 1 based on hierarchical clustering Yu K mean algorithm, feature exist
In: in the step Step4, result verification method are as follows: both algorithms are analyzed using profile width and Dunne's index
Effect is verified.
6. the weather data analysis method according to claim 1 based on hierarchical clustering Yu K mean algorithm, feature exist
In: the calculation of the profile width are as follows:
Wherein, aiIt is the average distance in i and same cluster between every other observation, biIt is to be seen in i and nearest neighbor cluster
Average distance between measured value, biCalculation formula are as follows:
Wherein, C (i) is the cluster comprising observation data i, and dist (i, j) is the distance between observation i and j, and n (C) is cluster
The radix of C.
7. the weather data analysis method according to claim 1 based on hierarchical clustering Yu K mean algorithm, feature exist
In: the calculation of Dunne's index are as follows:
Wherein, diam (Cm) it is cluster CmMaximum distance between middle observation.
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| CN110244099A (en) * | 2019-06-24 | 2019-09-17 | 河南工业大学 | Stealing detection method based on user's voltage |
| CN110647538A (en) * | 2019-10-18 | 2020-01-03 | 成都淞幸科技有限责任公司 | SOA-based climate observation data high-speed synthesis analysis method |
| CN110826623A (en) * | 2019-11-04 | 2020-02-21 | 深圳雷霆应急科技有限公司 | Classification method and device based on meteorological data, computer equipment and storage medium |
| CN115796564A (en) * | 2023-02-13 | 2023-03-14 | 山东济矿鲁能煤电股份有限公司阳城煤矿 | Coal mine work management system based on meteorological supervision |
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| CN105891629A (en) * | 2016-03-31 | 2016-08-24 | 广西电网有限责任公司电力科学研究院 | Transformer equipment fault identification method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110244099A (en) * | 2019-06-24 | 2019-09-17 | 河南工业大学 | Stealing detection method based on user's voltage |
| CN110647538A (en) * | 2019-10-18 | 2020-01-03 | 成都淞幸科技有限责任公司 | SOA-based climate observation data high-speed synthesis analysis method |
| CN110826623A (en) * | 2019-11-04 | 2020-02-21 | 深圳雷霆应急科技有限公司 | Classification method and device based on meteorological data, computer equipment and storage medium |
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| CN115796564A (en) * | 2023-02-13 | 2023-03-14 | 山东济矿鲁能煤电股份有限公司阳城煤矿 | Coal mine work management system based on meteorological supervision |
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