data preprocessing

"data preprocessing"的翻譯和解釋

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This paper divides the web usage mining process into three main parts : data collecting consists of the three server logs - access , referer , and agent , the html files the make up the site , and registration data ; data preprocessing , includes data cleaning , user identification , session identification , path completion and transaction identification ; data analysis consists of mining frequent access paths and association rule . thinking about the amount of hits on homepage , this paper improves the algorithm of finding frequent items
并結(jié)合網(wǎng)頁(yè)特點(diǎn)，考慮到主頁(yè)的點(diǎn)擊率的影響，對(duì)生成頻繁訪問(wèn)瀏覽頁(yè)的算法做了改進(jìn)；在web使用挖掘的基礎(chǔ)上引入部分web結(jié)構(gòu)挖掘，對(duì)挖掘?yàn)g覽頁(yè)的關(guān)聯(lián)規(guī)則做了補(bǔ)充，在web結(jié)構(gòu)挖掘基礎(chǔ)上挖掘出的相關(guān)瀏覽頁(yè)也推薦給用戶，在一定程度上提高了關(guān)聯(lián)規(guī)則的精確度。
In the data preprocessing step , by removing redundant data , dispersing inlet and outlet circle , offsetting the blade profile , data format is unified . in the case that measured profiles match theory profiles , each error item is attained by using appropriate arithmetic
數(shù)據(jù)預(yù)處理部分經(jīng)過(guò)葉片理論型線的冗余點(diǎn)剔除,進(jìn)、出汽邊圓弧離散以及理論型線的偏置等步驟,可實(shí)現(xiàn)理論型線與實(shí)測(cè)型線數(shù)據(jù)格式的統(tǒng)一;最后通過(guò)葉片的理論型線和實(shí)測(cè)型線重合度匹配等算法得到實(shí)測(cè)葉片的各主要誤差項(xiàng)。
In this paper , the methods of eos / modis lib data processing are discussed based on the format and the technical reports of modis ib datasets , including data preprocessing , data extracting , data calibrating and projection transformation etc . some problems in data processing are solved . a method of de _ striping , edf adjusting , is adopted . and the earth location data matching between different resolution pixels are discussed including data interpolating
本文根據(jù)eos modis1b資料的格式和內(nèi)容，詳細(xì)論述了modis1b數(shù)據(jù)集的處理方法，包括資料預(yù)處理、數(shù)據(jù)提取、資料定標(biāo)和投影變換等等，對(duì)于數(shù)據(jù)處理過(guò)程中可能存在的一些問(wèn)題給出了具體的解決方案，重點(diǎn)討論了定標(biāo)過(guò)程中去除波段圖像“條紋”的方法和投影過(guò)程中各種分辨率象素點(diǎn)的定位匹配以及由此帶來(lái)的定位數(shù)據(jù)插值等問(wèn)題。
Then the thesis further analyses some core techniques including the system of database , data warehouse and data mining and so on , and presents the frame of function of bank crm . the thesis puts its emphasis on the research on the data preprocessing of data warehouse , data copying , data cleansing , data integration and quality verifying included . finally the thesis discusses the key technology of data warehouse in bank crm - the cleansing of data of customers , and presents some methods of cleansing aiming at noisy values , missing values , conflicting values and duplicated values
本文在充分分析銀行crm的需求的基礎(chǔ)上，提出了基于數(shù)據(jù)倉(cāng)庫(kù)的銀行crm系統(tǒng)的體系結(jié)構(gòu)，并進(jìn)一步分析了該體系結(jié)構(gòu)中客戶數(shù)據(jù)庫(kù)系統(tǒng)、數(shù)據(jù)倉(cāng)庫(kù)、數(shù)據(jù)挖掘等核心技術(shù)組件的內(nèi)涵，給出了銀行crm系統(tǒng)的功能構(gòu)架；重點(diǎn)研究了銀行業(yè)務(wù)系統(tǒng)多年積累的客戶數(shù)據(jù)向數(shù)據(jù)倉(cāng)庫(kù)遷移的預(yù)處理方法和過(guò)程，其過(guò)程包括數(shù)據(jù)復(fù)制、數(shù)據(jù)清洗轉(zhuǎn)換、數(shù)據(jù)集成、質(zhì)量檢驗(yàn)和數(shù)據(jù)裝載；最后討論了銀行crm系統(tǒng)應(yīng)用數(shù)據(jù)倉(cāng)庫(kù)的關(guān)鍵技術(shù)：客戶數(shù)據(jù)清洗，給出了針對(duì)噪聲數(shù)據(jù)、空缺數(shù)據(jù)、不一致數(shù)據(jù)和重復(fù)數(shù)據(jù)的清洗方法。
However , the second technology has the following disadvantages : first , data paging and tmrm generating are integrated into one modulate , next , data structures they used are very complicated and large , in addition , the work of data preprocessing is very heavy and frequently data paging need the server with high performance , at last this paging method is very difficult for implementation . as for the first technology , an important advantage of it is that the data paging and tmrm generating are not interdependent , so it will be more e asily applied in practice than the second one . as an implementation of the first technology , lindstrom introduced a method which uses quadtree and triangle binary tree to organize terrain data and adopt multithread mechanism to realize the data process
而第一種流式處理技術(shù)盡管一次調(diào)入的數(shù)據(jù)量稍大，但其數(shù)據(jù)調(diào)度與多分辨率模型的生成在功能上是相互獨(dú)立的，如果處理得當(dāng)更容易在實(shí)際工程中得到成功的應(yīng)用，對(duì)于它的實(shí)現(xiàn)， lindstrom提出了利用四叉樹(shù)及三角形二叉樹(shù)進(jìn)行地形數(shù)據(jù)組織并利用多線程機(jī)制進(jìn)行數(shù)據(jù)調(diào)度與簡(jiǎn)化的流式處理方法，但該方法的缺點(diǎn)是：其數(shù)據(jù)結(jié)構(gòu)依賴于地形的物理分割，因此數(shù)據(jù)結(jié)構(gòu)龐大；多分辨率模型生成的計(jì)算量也依賴于物理分割的粒度，即物理分割粒度較粗時(shí)，數(shù)據(jù)范圍增大，計(jì)算量會(huì)急劇增加；該方法不能實(shí)現(xiàn)模型的增量生成。
We discuss several methods of spatial data preprocessing : empty data filling , noise data processing , data reduction , spatial characteristic generalizaion based on attribute induction , and sequent data discretization , which includes common discretized methods , gis attribute data reduction based on rough theory , and attribute data generalization based on cloud theory
2討論了幾種空間數(shù)據(jù)預(yù)處理方法：空缺數(shù)據(jù)填充、噪音數(shù)據(jù)處理、數(shù)據(jù)規(guī)約、連續(xù)屬性的離散化以及基于面向?qū)傩詺w納的空間特征概化。其中連續(xù)屬性離散化包括常規(guī)離散化方法、基于rough理論的gis屬性數(shù)據(jù)約簡(jiǎn)和基于云理論的屬性數(shù)據(jù)泛化。
So it is necessary to examine calcium activity and distribution in nerve cells . a way of visualizing intracellular ca2 + in three dimensional was established by using laser scanning confocal microscopy ( lscm ) and computer visualization technique in this paper . based on this way , which includes cell culturing and dyeing , confocal microscopy optimizing , confocal data preprocessing , 3d visualization of ca2 + by computer , we investigated the ca2 * distribution in cultured hippocampal neurons under different objectives
本文通過(guò)激光掃描共聚焦顯微技術(shù)和計(jì)算機(jī)三維可視化技術(shù)建立了一套神經(jīng)細(xì)胞內(nèi)鈣離子分布三維可視化的方法，包括細(xì)胞的培養(yǎng)和染色、顯微鏡參數(shù)的優(yōu)化、共聚焦數(shù)據(jù)的預(yù)處理、針對(duì)鈣離子的三維可視化方法的實(shí)現(xiàn)，為胞內(nèi)鈣離子作用機(jī)制的研究提供直觀的手段。
Surface reconstruction is one of key techniques in reverse engineering . based on unorganized discrete points come from finite element analysis triangular mesh on exterior surface of the aeroengine hollow turbine blade , this dissertation studied some related problems , such as scattered data preprocessing , quadrangle partition and surface reconstruction technology
曲面重構(gòu)是逆向工程研究的重要內(nèi)容之一，本文基于航空發(fā)動(dòng)機(jī)空心渦輪葉片外表面的有限元三角網(wǎng)格離散點(diǎn)，對(duì)離散數(shù)據(jù)點(diǎn)的預(yù)處理、四邊域劃分及曲面重構(gòu)技術(shù)進(jìn)行了研究。
Chapter five discussed the general process of development of data mining statistical information system based on jjms , including design of multi - dimension business space , design of method base , construction of data sources , data preprocessing , system architecture and so on . at last it discussed the effect , shortage , improvement of jjms and the conclusion and elicitation
第五章結(jié)合“紀(jì)檢監(jiān)察統(tǒng)計(jì)分析系統(tǒng)”討論了將描述數(shù)據(jù)挖掘技術(shù)應(yīng)用于統(tǒng)計(jì)信息系統(tǒng)時(shí)的一般思路，包括多維業(yè)務(wù)空間的設(shè)計(jì)、方法庫(kù)的設(shè)計(jì)、數(shù)據(jù)源的建設(shè)、數(shù)據(jù)預(yù)處理、系統(tǒng)架構(gòu)設(shè)計(jì)等內(nèi)容，最后對(duì)系統(tǒng)運(yùn)行效果、系統(tǒng)的不足和改進(jìn)、結(jié)論和啟發(fā)進(jìn)行了論述。
This thesis aims to discuss the clustering techniques with the background of large - scale nuclear physics science data mining . first , we introduce the key techniques and the main task in data mining , then we analyze the data preprocessing techniques and clustering techniques combine data mining techniques with science data . from data preprocessing aspect , we propose some methods of segmenting , denoising , integrating and transforming , and we use “ truncation method ” and “ successive difference method ” in data reduction , at last we extract information from the science data
論文基于大規(guī)模核物理科學(xué)數(shù)據(jù)挖掘的背景,全面介紹了數(shù)據(jù)挖掘的關(guān)鍵技術(shù)和主要任務(wù),從理論、算法和應(yīng)用三個(gè)層次,結(jié)合科學(xué)數(shù)據(jù)的特點(diǎn)來(lái)分析預(yù)處理技術(shù)和聚類方法,提出了很多實(shí)用的預(yù)處理方法:對(duì)hdf5科學(xué)數(shù)據(jù)進(jìn)行分塊、除噪、集成、變換等,同時(shí)對(duì)它使用“截?cái)喾ā焙汀爸饘忧蟛罘ā边M(jìn)行規(guī)約,并對(duì)數(shù)據(jù)進(jìn)行信息提取。