data mining tools 中文意思是什麼
data mining tools
解釋
數據挖掘工具-
This article mainly contains following several contents : ( 1 ) analyzed the connotation and the tools of knowledge management as well as the combine - relation of e - government - affairs system, and proposed the system framework electronic government - affairs system based on the knowledge management ( km ) ; ( 2 ) some crucial technologies in knowledge management are studied : knowledge representation technology, knowledge discursion technology as well as data mining technology
本文主要涉及以下幾個方面的內容: （ 1 ）分析了知識管理的內涵、工具以及與電子政務系統的結合關系，提出了基於知識管理（ knowledgemanagement ，簡稱km ）的電子政務系統的體系結構; （ 2 ）對知識管理中的幾個關鍵技術進行了研究:知識表達技術、知識推理技術以及數據挖掘技術。 -
Himalaya tools is a suite of programs focusing on new techniques in data mining
這些工具提供新的數據挖掘技術。 -
Specialized data mining tools containing intelligent agents are used to perform these tasks
專業的數據挖掘功能包括用來實施該任務的智能代理。 -
During the procedure of system design and implementation, the author has made some innovative efforts such as : ( d establishing the user interest orientated model, the model receiving user interests continuously and conjecturing user interests by interaction with the user, accumulating user preferences in information demand, thereby achieving self - adaptive retrieval, ? roviding a feedback method which is based on the human - machine interaction, summarizing the user operations on the interface of result presentation, and designing an algorithm for capturing user operation behaviors, by which the changes in user interests and preferences can be learned potentially, ? ffering a method for user interest mining which can extract subjects of information confirmed by user, thereby conjecturing or predicting different kinds of expressions of the same interest or extracting the new interests or unexpressed interests, ? roposing a solution of personalized internet information retrieval based on the user interests in accordance with the above - mentioned work, the solution having very strong feasibility and practicality with taking user interest model as center, employing machine learning ( active learning and passive learning ) and data mining as tools, and being assisted with network robot,
Piirs系統分析與設計過程中所做的創新性的嘗試主要有以下幾個方面：實現了基於用戶興趣的用戶模型，該模型通過與用戶的交互（主動交互和被動交互） ，不斷地接收用戶的興趣和推測用戶的興趣，積累用戶信息需求的偏好，實現自適應的檢索；提供了一種基於人機交互的反饋方法，對用戶在結果呈現界面上的操作進行了歸納總結，設計了用戶操作捕獲演算法， 「隱性地」學習用戶興趣和偏好的變化；提供了一種用戶需求挖掘的方法，對用戶已確定的信息做進一步的主題挖掘，由此推測或預測用戶同一興趣的不同表述方式或者挖掘出用戶新的或未表達出來的興趣；在上述工作基礎上提出了一套完整的基於用戶興趣的個性化網路信息檢索的解決方案，該方案以用戶興趣模型為中心，以機器學習（主動學習和被動學習）和數據挖掘為手段，輔以網路機器人，具有很強的可行性和實用性。 -
This thesis expatiates on the state - of - the - art of dm technique, with emphasis on data mining algorithms such as clustering analysis, classification analysis, dependence analysis and statistical analysis. a comparative study of three popular dm tools ( ibm intelligent miner, spss clementine and sas enterprise miner ) is carried out. the future trends of dm technology are also revealed
論文闡述了數據挖掘技術在國內外的研究現狀，對目前主要的數據挖掘演算法如聚類分析、分類分析、相關分析和統計分析進行了剖析，對當前最為流行的數據挖掘工具ibmintelligentminer 、 spssclementine及sasenterpriseminer進行比較分析，闡述了數據挖掘技術的未來發展趨勢。 -
Using the data mining tools
使用數據挖掘工具 -
Data mining tools including wizards, editors, query builders, lift chart
數據挖掘工具（包括向導、編輯器、查詢生成器、提升圖） -
As a result of these work, a data mining tools based on association rules, arminer, is introduced
這些工作的結晶就是一個基於關聯規則的數據挖掘工具arminer 。 -
Most knowledge discovery or data mining tools and techniques are based on statistics, machine learning, pattern recognition or artificial neural networks
大多數的知識發現或數據挖掘工具和技術是基於傳統的統計、機器學習、模式識別或人工神經網路。 -
This section provides information about how to work with the data mining tools in data mining designer in microsoft sql server 2005 analysis services ssas
本節提供了有關如何使用microsoft sql server 2005 analysis services （ ssas ）的數據挖掘設計器中的數據挖掘工具的信息。 -
The massive amount of high - throughput microarray, snps and other biological data bring a great challenge of developing advanced statistical and computational data mining tools
大量的高產能微陣列、單點核苷酸多型性及其他生物資料也對高級統計計算之資料探勘工具產生極大的挑戰。 -
In the last, the author discussed the design and realization process of waybill information synthetic application system in client. this system is designed in detail according to the user demanding, the data mode and specialist knowledge in repository, and some of soft engineering standards. it becomes an application including such as inquiring, statistic, data mining and analyzing function by using developing tools to programme, and it ' s the final production of study on the application of data mining and data warehouse technology
論文最後論述了貨票信息前臺綜合應用系統的設計與實現過程，該系統根據用戶需求、知識庫中的數據模式與專家知識，以及相關的軟體工程標準進行詳細設計，利用開發工具編程實現，集查詢、統計、數據挖掘與分析於一體，是數據挖掘與數據倉庫技術應用研究的最終成果。 -
This tutorial walks you through scenarios for targeted mailing, forecasting, market basket analysis, and sequence clustering, to demonstrate how to use the data mining algorithms, mining model viewers, and data mining tools that are included in microsoft sql server 2005 analysis services ssas
本教程將指導您演練目標郵件、預測、購物籃分析以及順序分析和聚類分析等方案，闡釋如何使用microsoft sql server 2005 analysis services （ ssas ）提供的數據挖掘演算法、挖掘模型查看器以及數據挖掘工具。 -
Mining data between people characters and their actions is a important aspect for multidimensional association rules. for example, association trend between students ' s nature information and their behavior. but many general mining tools have not paid much attention to these aspects
對于群體的特徵與行為的數據挖掘是關聯規則挖掘的一種重要的也是復雜的挖掘方向。例如在學生系統中的學生個體自然信息與他們的選課行為傾向之間的關聯傾向，商業領域中的顧客基本信息與購買傾向也屬于這類情況。 -
The key to the data mining technology described herein is to convert existing web pages into xml, or perhaps more appropriately xhtml, and use a few of the many tools for working with data structured as xml to retrieve the relevant data
這里描述的數據挖掘技術的關鍵是把現有的web頁面轉換成xml ，或轉換成xhtml可能更適當，並使用眾多工具中的一小部分來處理xml結構的數據，以檢索出適當的數據。 -
Specifically, aiming at two widely used algorithms in data mining, naive bayesian classifier and boolean association apriori algorithm. we have brought forward two corresponding protocols incorporating privacy concerns. we have used secure multi - party computation protocols and tools to get the solutions
本文針對數據挖掘中應用較為廣泛的樸素貝葉斯分類器和關聯規則的apriori演算法，利用安全多方計算的理論和工具，給出了與其相應的隱私性演算法。 -
Classification has always been a central issue on data mining, machine learning and pattern recognition, classifier, as an important model and method of machine learning and data mining, is very important to the development and application of machine learning and the data mining. the classifier ’ s effect closely correlates with the characteristic of data sets, at present, the construction of classifier is generally based on the character of different datasets, there is no such a classifier which is suitable for any data sets. under uncertain conditions, the bayes network is a powerful tools for the knowledge expression and inference, but for difficulties in constructing its network structure and very high time complexity, it has not been considered as a classifier algorithm until the emergence of na ? ve - bayes classifier
分類一直是數據挖掘、機器學習和模式識別等研究的核心問題，貝葉斯網路是作為知識表示和推理的強大工具，由於搜索空間巨大和學習困難的原因，直到樸素貝葉斯理論的出現才被作為分類器演算法，改進樸素貝葉斯分類器是貝葉斯分類器學習的一個主要的研究方向。遺傳演算法本質上是一種求解問題的高效并行全局搜索演算法，適合應用於那些改進的分類器的結構學習中。本文提出了一種基於遺傳演算法的ban分類器演算法。 -
Xml / rdf can explicitly describe the unite, structure and formalization of different sorts web information sources, and it considers the objects of web environment as < wp = 8 > resources and sets down unambiguous grammar and semantic, meanwhile it makes us research and develop new web mining technologies and use traditional mining algorithms and tools to carry out specific and multi - arrangement data mining, based on programming and structure data
Xml / rdf能夠明確描述網上各種信息源的統一性、結構性和規范化，它把網路環境中的對象視為資源，並制定了明確的描述性語法和語義，使我們能夠在一個規劃化、結構化的統一數據層面上，研究和開發新的網路挖掘技術，同時可以運用傳統的挖掘演算法和工具對各種目標資源進行特定的多種層次的綜合數據挖掘。 -
In part of the data warehouse technology, the background, the concept, the architecture, the data structure, the data mode, the pivotal technology and the establishing process of data warehouse are introduced. with regard to the data mining technology, the thesis sets forth the concept and characteristic, object mined, typical methods and tools used of data mining, and specifies the process of data mining. the concept and characteristic, the multiple dimensional data structure, the data processing mode and analytical technique of olap technology are depicted concerning the olap technology, and the architecture and data organization of the olap system are presented in detail
在數據倉庫技術部分，介紹了數據倉庫的產生背景、概念、體系結構、數據組織結構、數扼組織方式、數據倉庫的關鍵技術以及數據倉庫的創建步驟；在數據挖掘技術部分，介紹了數據挖掘的概念及特點、數據挖掘的目標、使用的典型方法及工具，重點闡述了數據挖掘的過程；在olap技術部分，介紹了olap的概念及特徵、 olap的多維數據結構、數據處理方式和分析方法，重點闡述了olap系統的體系結構和數據組織的三種方式。 -
Analyze its function point and information process. discuss its call center ' s technologic solution and function framework. research the question about how to use the customers " information, to guide future merchandise planning, with the tools of data warehouse and data mining method
對其功能、信息流程和呼叫中心的架構、提供定製服務的商務網站構架進行了設計，並針對大批量定製所擁有的海量數據如何進行深度挖掘，從而對銷售進行輔助決策進行了研究。
分享友人