發(fā)布時間：2018-09-07 12:34

【摘要】：隨著信息技術(shù)的進步,數(shù)據(jù)量呈現(xiàn)爆炸式增長,傳統(tǒng)的基于CPU的數(shù)據(jù)挖掘技術(shù)已經(jīng)不能高效地處理如此巨大的數(shù)據(jù)量了。此外,人的大腦對于枯燥的數(shù)字更容易識別顏色和幾何圖形,利用數(shù)據(jù)可視化技術(shù)可以將數(shù)據(jù)挖掘結(jié)果更加自然和直觀地呈現(xiàn)在操作界面,可以更好地滿足用戶的需求。但目前,數(shù)據(jù)挖掘最常用的傳統(tǒng)數(shù)據(jù)可視化工具只能繪制二維或三維圖形,且缺乏互動性�；�于上述問題本文提出了一個基于并行計算的交互式數(shù)據(jù)挖掘和可視化系統(tǒng)。本文提出了利用GPU(Graphics Processing Unit)編程的方式對經(jīng)典的數(shù)據(jù)流挖掘算法進行優(yōu)化,傳統(tǒng)的基于CPU的數(shù)據(jù)挖掘技術(shù)采用串行的數(shù)據(jù)處理方式,無法滿足多個計算機資源同時運行的需求,當(dāng)數(shù)據(jù)量較大時,處理時迭代次數(shù)會很多,內(nèi)存需求較大,處理速度會很慢,效率較低。而GPU編程方式采用的是并行的方式處理數(shù)據(jù),多個線程相互獨立同時運行,運算效率很高,更加適應(yīng)于處理大量數(shù)據(jù)。本文針對大數(shù)據(jù)中數(shù)據(jù)獨立性情況和數(shù)據(jù)依賴性情況,分別利用GPU編程技術(shù)對數(shù)據(jù)挖掘中聚類算法K-Means和連通區(qū)域標(biāo)記算法(Connected Component Labeling,CCL)進行優(yōu)化,更好地完成了對大數(shù)據(jù)的挖掘分析。本文提出了交互式的數(shù)據(jù)可視化方法,為了實現(xiàn)對數(shù)據(jù)的可視化,我們利用DirectX的軟件開發(fā)工具包,將原始數(shù)據(jù)集或數(shù)據(jù)挖掘結(jié)果轉(zhuǎn)換為頂點、線、面、顏色和其他圖形等信息,利用軟件開發(fā)工具包中提供的各種清晰明了的圖形函數(shù)建立多維模型,并對最后的可視化結(jié)果進行渲染。此外,我們還創(chuàng)建了一個圖形用戶界面(GUI),用戶可以根據(jù)自己不同的需求,改變聚類的參數(shù),得到符合自己需求的可視化結(jié)果�；�于上述算法,本文對空調(diào)運行產(chǎn)生的能耗數(shù)據(jù)進行了實驗,通過使用GPU編程方式對傳統(tǒng)算法進行優(yōu)化,不僅實現(xiàn)了對數(shù)據(jù)的聚類分析,而且通過實驗數(shù)據(jù)證明了使用本系統(tǒng)處理巨大的數(shù)據(jù)量時運行速度得到很大提升,運算效率更高。此外,我們使用DirectX的軟件開發(fā)工具包將抽象的數(shù)據(jù)挖掘結(jié)果表示為具體的四維立體的圖形圖像,并且用戶還可以通過鍵盤操作改變可視化結(jié)果的觀察視角以及聚類的K值,得到自己想要的結(jié)果,滿足了用戶的真正需求。
[Abstract]:With the development of information technology, the amount of data increases explosively. The traditional data mining technology based on CPU can not deal with such a huge amount of data efficiently. In addition, the human brain is easier to recognize color and geometry for boring numbers. Using data visualization technology, data mining results can be more naturally and intuitively presented in the operation interface, which can better meet the needs of users. But at present, the traditional data visualization tools used in data mining can only draw 2D or 3D graphics, and lack of interactivity. This paper presents an interactive data mining and visualization system based on parallel computing. In this paper, the classical data stream mining algorithm is optimized by using GPU (Graphics Processing Unit) programming method. The traditional data mining technology based on CPU adopts serial data processing method, which can not meet the needs of multiple computer resources running at the same time. When the amount of data is large, the number of iterations will be many, the memory requirement will be large, the processing speed will be very slow and the efficiency will be low. The GPU programming method uses the parallel way to process the data. The multiple threads run independently and simultaneously, so the operation efficiency is very high, so it is more suitable to deal with a large amount of data. Aiming at the data independence and data dependence in big data, this paper optimizes the clustering algorithm K-Means and the connected area marking algorithm (Connected Component Labeling,CCL by using GPU programming technology, and completes the mining analysis of big data. In this paper, an interactive method of data visualization is proposed. In order to realize the visualization of data, we use the software development kit of DirectX to transform the original data set or data mining result into vertex, line, surface, color and other graphics. The multi-dimensional model is built by using various clear graphic functions provided in the software development toolkit, and the final visualization results are rendered. In addition, we also create a graphical user interface (GUI),) which can change the clustering parameters according to their different requirements and get the visualization results that meet their needs. Based on the above algorithm, the energy consumption data generated by air conditioning operation are experimented in this paper, and the traditional algorithm is optimized by using GPU programming method, which not only realizes the clustering analysis of the data, The experimental data show that the speed of the system is greatly improved and the operation efficiency is higher when the system is used to deal with the huge amount of data. In addition, we use the software development kit of DirectX to represent the abstract data mining results as concrete four-dimensional three-dimensional graphics and images, and users can change the visual view of the visual results and the K value of clustering through keyboard operation. Get the results you want to meet the real needs of users.
【學(xué)位授予單位】：北方工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.13

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