本文選題：數(shù)據(jù)可視化 + 數(shù)據(jù)圖形化�。� 參考：《重慶大學(xué)》2015年碩士論文

【摘要】：大數(shù)據(jù)比較公認的概念是4V特點數(shù)據(jù)——變化速度快(Velocity)、數(shù)據(jù)量大(Volume)、價值密度低(Value)、數(shù)據(jù)類型多樣化(Variety),這意味著快速且低成本的處理、巨大的數(shù)據(jù)量、多樣化的來源。大數(shù)據(jù)給人們帶來了機遇和挑戰(zhàn),但是數(shù)據(jù)給人的直觀感受總是冰冷枯燥,讓人望而生畏,百思不得其解。為了使數(shù)據(jù)生動有趣,讓數(shù)據(jù)使用者一目了然,豁然開朗,需要我們采用一些特別的方式展示數(shù)據(jù),來解釋、分析及應(yīng)用數(shù)據(jù),而且使得其能有效傳播,這就是數(shù)據(jù)可視化技術(shù)。數(shù)據(jù)可視化中運用圖形化,主要目的在于對復(fù)雜數(shù)據(jù)信息的更直觀解釋。信息圖形化主要步驟如下:獲取、解析、過濾、挖掘、展示、總結(jié)。這個過程需要研究人員對多種專業(yè)技能熟練掌握。從獲取數(shù)據(jù)開始,研究人員首先需要解決數(shù)據(jù)易讀的問題。數(shù)據(jù)挖掘和展示時,則需要研究人員挖掘數(shù)據(jù)的本質(zhì)特征、模式等�？芍�,描述統(tǒng)計是表述數(shù)據(jù)內(nèi)在含義的一個大類。數(shù)據(jù)作為信息存在的重要形式,在人們的工作生活中所起到的作用越來越大,而計算機技術(shù)的發(fā)展則使人們越來越依賴各種計算機化的數(shù)據(jù)。一方面計算機的應(yīng)用領(lǐng)域幾乎遍及各行各業(yè)(科研、工程、管理、醫(yī)學(xué)、電子商務(wù)、金融等),另一方面,計算機處理的數(shù)據(jù)量也呈幾何級數(shù)增長,不僅數(shù)據(jù)采集能力和手段日趨多元化,存儲設(shè)備技術(shù)也發(fā)展迅猛,為人們在大數(shù)據(jù)時代實現(xiàn)海量數(shù)據(jù)的充分應(yīng)用創(chuàng)造了條件。面對大量且龐雜的數(shù)據(jù)信息,如何從中提取出有價值且便于觀察的信息是目前最迫切的問題。顯然要解決上訴的問題,僅僅采用統(tǒng)計數(shù)據(jù)分析方法容易引起數(shù)據(jù)的不易理解,因此,筆者將統(tǒng)計數(shù)據(jù)分析技術(shù)和數(shù)據(jù)圖形化方法結(jié)合起來,力求實現(xiàn)數(shù)據(jù)分析的可讀性,易讀性。從統(tǒng)計數(shù)據(jù)分析方法和圖形化技術(shù)的統(tǒng)計學(xué)基礎(chǔ)入手,研究統(tǒng)計數(shù)據(jù)圖形化的表示方法,并在此基礎(chǔ)上進行統(tǒng)計數(shù)據(jù)分析及圖形化技術(shù)在具體經(jīng)濟數(shù)據(jù)中的應(yīng)用。本文的研究內(nèi)容分為兩部分:①常見數(shù)據(jù)圖形化方法及其應(yīng)用,這部分主要整理了常用的統(tǒng)計數(shù)據(jù)圖形方法,如條形圖、直方圖、散點圖、餅圖、線圖、面積堆積圖等。介紹了這些圖形的做法,功能及應(yīng)用。在此基礎(chǔ)上對圖形的著色、大小、形狀等圖形屬性以及標(biāo)度注解各方面在計算機上進行綜合運用。②多維數(shù)據(jù)圖形化方法及應(yīng)用,收集整理多維股票數(shù)據(jù),在此基礎(chǔ)上進行相關(guān)矩陣圖、輪廓圖、星圖、臉譜圖以及譜系圖的圖形展示,并對圖形展示結(jié)果進行分析。
[Abstract]:The generally accepted concept of big data is 4V characteristic data--fast changing speed, large volume of data, low value density and diversified data types, which means fast and low cost processing, huge amount of data, and diversified sources. Big data brings people opportunities and challenges, but the intuitive feeling of data is always cold, boring and daunting. In order to make the data lively and interesting, to make the data user clear and clear, we need to show the data in some special way, to interpret, analyze and apply the data, and to make it spread effectively. This is data visualization technology. The use of graphics in data visualization is mainly aimed at the more intuitive interpretation of complex data information. The main steps of graphic information are as follows: access, analysis, filtering, mining, display, summary. This process requires researchers to be proficient in a variety of professional skills. Starting with getting data, researchers first need to solve the problem of readability. When mining and displaying data, researchers need to mine the essential characteristics and patterns of data. It can be seen that descriptive statistics is a large class that describes the intrinsic meaning of data. As an important form of information, data plays a more and more important role in people's work and life, and the development of computer technology makes people rely more and more on computerized data. On the one hand, the application of computers is almost universal in all walks of life (scientific research, engineering, management, medicine, electronic commerce, finance, etc.). On the other hand, the amount of data processed by computers also increases in a geometric series. Not only the ability and means of data acquisition are becoming more and more diverse, but also the technology of storage devices is developing rapidly, which creates conditions for people to realize the full application of massive data in the era of big data. In the face of a large amount of data information, how to extract valuable and easy to observe information is the most urgent problem. Obviously, to solve the problem of appeal, it is easy to understand the data by using the statistical data analysis method only. Therefore, the author combines the statistical data analysis technology with the data graphic method to realize the readability of the data analysis. Readability. Based on the statistical basis of statistical data analysis method and graphic technique, this paper studies the graphical representation method of statistical data, and on this basis carries on the statistical data analysis and the application of graphic technology in the concrete economic data. The research content of this paper is divided into two parts: 1 common data graphic method and its application. This part mainly arranges the commonly used statistical data graph method, such as bar chart, histogram, scattered plot, pie chart, graph, area stacking diagram and so on. The methods, functions and applications of these graphics are introduced. On the basis of this, the graphics attributes, size, shape, and scale annotation of the graphics are comprehensively applied to the computer with .2 dimensional data graphical method and application, and the multidimensional stock data are collected and sorted out. On this basis, the graph display of correlation matrix map, contour map, star map, face map and pedigree diagram is carried out, and the result of graph display is analyzed.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：C81

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