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  本文選題：數(shù)據(jù)挖掘　切入點：關聯(lián)規(guī)則　出處：《天津工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著全球天氣的不斷變化,依靠天氣預報來及時發(fā)現(xiàn)災害天氣的出現(xiàn)顯得尤為重要。大數(shù)據(jù)時代的來臨,將數(shù)據(jù)挖掘技術應用于氣象預報中,分析各種氣象因子之間的關聯(lián),提高氣象預報的準確性,具有十分重要的現(xiàn)實意義。傳統(tǒng)的氣象預報是基于統(tǒng)計的預測模型,采用概率領域的相關方法將歷史數(shù)據(jù)建立一個或多個模型。但是,傳統(tǒng)的統(tǒng)計方法往往適用于大量預報對象,預報對象越多,找出的預報因子和預報對象之間的關聯(lián)越多,得到的統(tǒng)計結果越精確。然而,在實際應用中,往往需要針對某一特定天氣對象進行預報,傳統(tǒng)的統(tǒng)計方法存在一定的局限性。使用數(shù)據(jù)挖掘技術可以針對某一特定天氣對象,快速處理海量天氣數(shù)據(jù),挖掘出潛在的、人們不易發(fā)覺的預報因子之間的關聯(lián),有助于提高天氣預報的準確性。目前智能算法在數(shù)據(jù)挖掘領域的應用受到越來越多學者的關注。引力移動算法GMA是近年提出的一種啟發(fā)式群體優(yōu)化算法,性能比傳統(tǒng)粒子群算法有著很大提高,但仍然存在著缺陷。為提高引力移動算法搜索性能,針對引力移動算法解決一些高維空間優(yōu)化問題時存在的收斂速度慢、搜索精度不高的問題,本文提出一種基于親和度的改進引力移動算法PGMA,即基于引力移動算法原理,通過構造一個基于親和度概念的系數(shù),對種群個體受到的引力合力公式作適當?shù)淖儞Q來改造基本引力移動算法。改進后的算法對種群中個體的位置更新方向加以引導,提高算法的搜索精度和算法搜索能力。用13個基準函數(shù)對改進算法進行試驗,驗證了改進算法在求解精度和穩(wěn)定性上優(yōu)于基本引力移動算法。然后將PGMA算法應用到了關聯(lián)規(guī)則挖掘領域,并通過實驗證明其性能在關聯(lián)規(guī)則挖掘領域中的提高,并將對這種關聯(lián)規(guī)則挖掘方案進行獨立性檢驗改進并應用到氣象預測領域中。通過上海市氣象數(shù)據(jù)集證明了具有獨立性檢驗的TI-PGMA關聯(lián)規(guī)則挖掘方案的準確性和有效性。
[Abstract]:With the continuous changes of the global weather, it is very important to rely on the weather forecast to discover the occurrence of the disaster weather in time. With the advent of big data era, the data mining technology is applied to the weather forecast, and the correlation between various meteorological factors is analyzed. It is of great practical significance to improve the accuracy of meteorological forecast. The traditional forecasting model is based on statistics, and the historical data are built into one or more models by using the method of probability domain. Traditional statistical methods are often applicable to a large number of forecasting objects. The more forecasting objects, the more correlation between the prediction factors and the prediction objects, the more accurate the statistical results are. However, in practical applications, It is often necessary to forecast a particular weather object, but the traditional statistical method has some limitations. Using data mining technology, we can quickly process the massive weather data for a particular weather object, and find out the potential weather data. The link between predictors that people are not easily aware of, At present, more and more scholars pay attention to the application of intelligent algorithm in the field of data mining. The gravitational movement algorithm (GMA) is a heuristic group optimization algorithm proposed in recent years. In order to improve the search performance of gravity moving algorithm, the convergence rate of gravity moving algorithm to solve some high-dimensional spatial optimization problems is slow. In this paper, an improved gravitational mobility algorithm based on affinity, PGMA, is proposed, which is based on the principle of gravitational mobility, and a coefficient based on the concept of affinity is constructed. An appropriate transformation of the formula of gravitational resultant force to the population individual is made to transform the basic gravity moving algorithm. The improved algorithm guides the orientation of the updating of the individual's position in the population. Improve the search accuracy and search ability of the algorithm. The improved algorithm is tested with 13 benchmark functions. The improved algorithm is superior to the basic gravity moving algorithm in solving accuracy and stability. Then the PGMA algorithm is applied to the field of association rule mining, and the performance of the improved algorithm in association rule mining field is proved by experiments. This paper improves the independence test of this association rule mining scheme and applies it to the field of meteorological prediction. The accuracy and validity of the TI-PGMA association rule mining scheme with independence test are proved by using the Shanghai Meteorological data set.
【學位授予單位】：天津工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P409;TP311.13

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本文編號：1583855