Introduction to Data Mining

Introduction to Data Mining

Abstract:Microsoft? SQL Server? 2005 provides an integrated environment for creating and working with data mining models. This tutorial uses four scenarios, targeted mailing, forecasting, market basket, and sequence clustering, to demonstrate how to use the mining model algorithms, mining model viewers, and data mining tools that are included in this release of SQL Server.

Introduction

The data mining tutorial is designed to walk you through the process of creating data mining models in Microsoft SQL Server 2005. The data mining algorithms and tools in SQL Server 2005 make it easy to build a comprehensive solution for a variety of projects, including market basket analysis, forecasting analysis, and targeted mailing analysis. The scenarios for these solutions are explained in greater detail later in the tutorial.

The most visible components in SQL Server 2005 are the workspaces that you use to create and work with data mining models. The online analytical processing (OLAP) and data mining tools are consolidated into two working environments: Business Intelligence Development Studio and SQL Server Management Studio. Using Business Intelligence Development Studio, you can develop an Analysis Services project disconnected from the server. When the project is ready, you can deploy it to the server. Y ou can also work directly against the server. The main function of SQL Server Management Studio is to manage the server. Each environment is described in more detail later in this introduction. For more information on choosing between the two environments, see "Choosing Between SQL Server Management Studio and Business Intelligence Development Studio" in SQL Server Books Online.

All of the data mining tools exist in the data mining editor. Using the editor you can manage mining models, create new models, view models, compare models, and create predictions based on existing models.

After you build a mining model, you will want to explore it, looking for interesting patterns and rules. Each mining model viewer in the editor is customized to explore models built with a specific algorithm. For more information about the viewers, see "V iewing a Data Mining Model" in SQL Server Books Online.

Often your project will contain several mining models, so before you can use a model to create predictions, you need to be able to determine which model is the most accurate. For this reason, the editor contains a model comparison tool called the Mining Accuracy Chart tab. Using this tool you can compare the predictive accuracy of your models and determine the best model.

To create predictions, you will use the Data Mining Extensions (DMX) language. DMX extends SQL, containing commands to create, modify, and predict against mining models. For more information about DMX, see "Data Mining Extensions (DMX) Reference" in SQL Server

Books Online. Because creating a prediction can be complicated, the data mining editor contains a tool called Prediction Query Builder, which allows you to build queries using a graphical interface. Y ou can also view the DMX code that is generated by the query builder.

Just as important as the tools that you use to work with and create data mining models are the mechanics by which they are created. The key to creating a mining model is the data mining algorithm. The algorithm finds patterns in the data that you pass it, and it translates them into a mining model — it is the engine behind the process.

Some of the most important steps in creating a data mining solution are consolidating, cleaning, and preparing the data to be used to create the mining models. SQL Server 2005 includes the Data Transformation Services (DTS) working environment, which contains tools that you can use to clean, validate, and prepare your data. For more information on using DTS in conjunction with a data mining solution, see "DTS Data Mining Tasks and Transformations" in SQL Server Books Online.

In order to demonstrate the SQL Server data mining features, this tutorial uses a new sample database called AdventureWorksDW. The database is included with SQL Server 2005, and it supports OLAP and data mining functionality. In order to make the sample database available, you need to select the sample database at the installation time in the “Advanced” dialog for component selection.

Adventure Works

AdventureWorksDW is based on a fictional bicycle manufacturing company named Adventure Works Cycles. Adventure Works produces and distributes metal and composite bicycles to North American, European, and Asian commercial markets. The base of operations is located in Bothell, Washington with 500 employees, and several regional sales teams are located throughout their market base.

Adventure Works sells products wholesale to specialty shops and to individuals through the Internet. For the data mining exercises, you will work with the AdventureWorksDW Internet sales tables, which contain realistic patterns that work well for data mining exercises.

For more information on Adventure Works Cycles see "Sample Databases and Business Scenarios" in SQL Server Books Online.

Database Details

The Internet sales schema contains information about 9,242 customers. These customers live in six countries, which are combined into three regions:

North America (83%)

Europe (12%)

Australia (7%)

The database contains data for three fiscal years: 2002, 2003, and 2004.

The products in the database are broken down by subcategory, model, and product.

Business Intelligence Development Studio

Business Intelligence Development Studio is a set of tools designed for creating business intelligence projects. Because Business Intelligence Development Studio was created as an IDE environment in which you can create a complete solution, you work disconnected from the server. Y ou can change your data mining objects as much as you want, but the changes are not reflected on the server until after you deploy the project.

Working in an IDE is beneficial for the following reasons:

The Analysis Services project is the entry point for a business intelligence solution. An Analysis Services project encapsulates mining models and OLAP cubes, along with supplemental objects that make up the Analysis Services database. From Business Intelligence Development Studio, you can create and edit Analysis Services objects within a project and deploy the project to the appropriate Analysis Services server or servers.

If you are working with an existing Analysis Services project, you can also use Business Intelligence Development Studio to work connected the server. In this way, changes are reflected directly on the server without having to deploy the solution.

SQL Server Management Studio

SQL Server Management Studio is a collection of administrative and scripting tools for working with Microsoft SQL Server components. This workspace differs from Business Intelligence Development Studio in that you are working in a connected environment where actions are propagated to the server as soon as you save your work.

After the data has been cleaned and prepared for data mining, most of the tasks associated with creating a data mining solution are performed within Business Intelligence Development Studio. Using the Business Intelligence Development Studio tools, you develop and test the data mining solution, using an iterative process to determine which models work best for a given situation. When the developer is satisfied with the solution, it is deployed to an Analysis Services server. From this point, the focus shifts from development to maintenance and use, and thus SQL Server Management Studio. Using SQL Server Management Studio, you can administer your database and perform some of the same functions as in Business Intelligence Development Studio, such as viewing, and creating predictions from mining models.

Data Transformation Services

Data Transformation Services (DTS) comprises the Extract, Transform, and Load (ETL) tools in SQL Server 2005. These tools can be used to perform some of the most important tasks in data mining: cleaning and preparing the data for model creation. In data mining, you typically perform repetitive data transformations to clean the data before using the data to train a mining model.

Using the tasks and transformations in DTS, you can combine data preparation and model creation into a single DTS package.

DTS also provides DTS Designer to help you easily build and run packages containing all of the tasks and transformations. Using DTS Designer, you can deploy the packages to a server and run them on a regularly scheduled basis. This is useful if, for example, you collect data weekly data and want to perform the same cleaning transformations each time in an automated fashion.

Y ou can work with a Data Transformation project and an Analysis Services project together as part of a business intelligence solution, by adding each project to a solution in Business Intelligence Development Studio.

Mining Model Algorithms

Data mining algorithms are the foundation from which mining models are created. The variety of algorithms included in SQL Server 2005 allows you to perform many types of analysis. For more specific information about the algorithms and how they can be adjusted using parameters, see "Data Mining Algorithms" in SQL Server Books Online.

Microsoft Decision Trees

The Microsoft Decision Trees algorithm supports both classification and regression and it works well for predictive modeling. Using the algorithm, you can predict both discrete and continuous attributes.

In building a model, the algorithm examines how each input attribute in the dataset affects the result of the predicted attribute, and then it uses the input attributes with the strongest relationship to create a series of splits, called nodes. As new nodes are added to the model, a tree structure begins to form. The top node of the tree describes the breakdown of the predicted attribute over the overall population. Each additional node is created based on the distribution of states of the predicted attribute as compared to the input attributes. If an input attribute is seen to cause the predicted attribute to favor one state over another, a new node is added to the model. The model continues to grow until none of the remaining attributes create a split that provides an improved prediction over the existing node. The model seeks to find a combination of attributes and their states that creates a disproportionate distribution of states in the predicted attribute, therefore allowing you to predict the outcome of the predicted attribute.

Microsoft Clustering

The Microsoft Clustering algorithm uses iterative techniques to group records from a dataset into clusters containing similar characteristics. Using these clusters, you can explore the data, learning more about the relationships that exist, which may not be easy to derive logically through casual observation. Additionally, you can create predictions from the clustering model created by the algorithm. For example, consider a group of people who live in the same neighborhood, drive

the same kind of car, eat the same kind of food, and buy a similar version of a product. This is a cluster of data. Another cluster may include people who go to the same restaurants, have similar salaries, and vacation twice a year outside the country. Observing how these clusters are distributed, you can better understand how the records in a dataset interact, as well as how that interaction affects the outcome of a predicted attribute.

Microsoft Na?ve Bayes

The Microsoft Na?ve Bayes algorithm quickly builds mining models that can be used for classification and prediction. It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute, which can later be used to predict an outcome of the predicted attribute based on the known input attributes. The probabilities used to generate the model are calculated and stored during the processing of the cube. The algorithm supports only discrete or discretized attributes, and it considers all input attributes to be independent. The Microsoft Na?ve Bayes algorithm produces a simple mining model that can be considered a starting point in the data mining process. Because most of the calculations used in creating the model are generated during cube processing, results are returned quickly. This makes the model a good option for exploring the data and for discovering how various input attributes are distributed in the different states of the predicted attribute.

Microsoft Time Series

The Microsoft Time Series algorithm creates models that can be used to predict continuous variables over time from both OLAP and relational data sources. For example, you can use the Microsoft Time Series algorithm to predict sales and profits based on the historical data in a cube.

Using the algorithm, you can choose one or more variables to predict, but they must be continuous. Y ou can have only one case series for each model. The case series identifies the location in a series, such as the date when looking at sales over a length of several months or years.

A case may contain a set of variables (for example, sales at different stores). The Microsoft Time Series algorithm can use cross-variable correlations in its predictions. For example, prior sales at one store may be useful in predicting current sales at another store.

Microsoft Neural Network

In Microsoft SQL Server 2005 Analysis Services, the Microsoft Neural Network algorithm creates classification and regression mining models by constructing a multilayer perceptron network of neurons. Similar to the Microsoft Decision Trees algorithm provider, given each state of the predictable attribute, the algorithm calculates probabilities for each possible state of the input attribute. The algorithm provider processes the entire set of cases , iteratively comparing the predicted classification of the cases with the known actual classification of the cases. The errors from the initial classification of the first iteration of the entire set of cases is fed back into the

network, and used to modify the network's performance for the next iteration, and so on. Y ou can later use these probabilities to predict an outcome of the predicted attribute, based on the input attributes. One of the primary differences between this algorithm and the Microsoft Decision Trees algorithm, however, is that its learning process is to optimize network parameters toward minimizing the error while the Microsoft Decision Trees algorithm splits rules in order to maximize information gain. The algorithm supports the prediction of both discrete and continuous attributes.

Microsoft Linear Regression

The Microsoft Linear Regression algorithm is a particular configuration of the Microsoft Decision Trees algorithm, obtained by disabling splits (the whole regression formula is built in a single root node). The algorithm supports the prediction of continuous attributes.

Microsoft Logistic Regression

The Microsoft Logistic Regression algorithm is a particular configuration of the Microsoft Neural Network algorithm, obtained by eliminating the hidden layer. The algorithm supports the prediction of both discrete andcontinuous attributes.)

DataMining分析方法

如有你有帮助，请购买下载，谢谢！ 数据挖掘 Data Mining 第一部 Data Mining的觀念............... 错误！未定义书签。 第一章何謂Data Mining ..................................................... 错误！未定义书签。 第二章Data Mining運用的理論與實際應用功能............. 错误！未定义书签。 第三章Data Mining與統計分析有何不同......................... 错误！未定义书签。 第四章完整的Data Mining有哪些步驟............................ 错误！未定义书签。 第五章CRISP-DM ............................................................... 错误！未定义书签。 第六章Data Mining、Data Warehousing、OLAP三者關係為何. 错误！未定义书签。 第七章Data Mining在CRM中扮演的角色為何.............. 错误！未定义书签。 第八章Data Mining 與Web Mining有何不同................. 错误！未定义书签。 第九章Data Mining 的功能................................................ 错误！未定义书签。 第十章Data Mining應用於各領域的情形......................... 错误！未定义书签。 第十一章Data Mining的分析工具..................................... 错误！未定义书签。第二部多變量分析....................... 错误！未定义书签。 第一章主成分分析(Principal Component Analysis) ........... 错误！未定义书签。 第二章因素分析(Factor Analysis) ...................................... 错误！未定义书签。 第三章判別分析法(Discriminant Analysis) ........................ 错误！未定义书签。 第四章集群分析法(Cluster Analysis) ................................. 错误！未定义书签。 第五章典型相關分析(Canonical Correlation Analysis) ..... 错误！未定义书签。 第六章路徑分析(Path Analysis) .......................................... 错误！未定义书签。 第七章迴歸分析 .................................................................. 错误！未定义书签。 第一節何謂迴歸分析 .................................................. 错误！未定义书签。 第二節簡單線性迴歸模式 .......................................... 错误！未定义书签。 第三節羅吉斯迴歸模式(Logistic Regression) ............ 错误！未定义书签。第三部改良的Data Mining理論技術....... 错误！未定义书签。 第一章類神經網路(Artificial Neural Network, ANN) ....... 错误！未定义书签。 0页

力 扣 数 据 结 构 与 算 法

前端如何搞定数据结构与算法(先导篇) 「观感度：?」 「口味：锅包肉」 「烹饪时间：20min」 本文已收录在Github? 为什么要学习数据结构与算法？ 在0202年的今天，由于每天被无数的信息轰炸，大多数人已经变得越来越浮躁了，并且丧失了独立思考的能力。 你可能会经常听到这样的感慨： 技术人究竟能走多远？我遇到了天花板 35岁的程序员要如何面对中年危机？ 技术更新太快，好累，学不动了 然后，你也变得焦虑起来。那你有没有静下心来想过，如何才能抵御年龄增长并且使自己增值呢？ 无非是终身学习，持续修炼自己的内功。内功也就是基础知识和核心概念，这些轰轰烈烈发展的技术本质，其实都是基础知识，也就是我们在大学里学过的基础课-程。 操作系统 计算机组成原理 计算机网络 编译原理

设计模式 数据结构与算法 这也就是为什么越靠谱的面试官越注重你基础知识的掌握程度，为什么越牛的的企业越重视你的算法能力。因为当你拥有了这些，你已经比大多数人优秀了。你的天花板由你自己来决定，大家口中的中年危机可能并不会成为你的危机。新技术来临时，你对它的本质会看得更加透彻，学起来会一通百通。这样的人才，公司培养你也会花费更少的成本。 (不过，一辈子做个开开心心的 CRUD Boy 也是一种选择。) 数据结构与算法之间的关系 Rob Pikes 5 Rules of Programming中的第五条是这样说的： Data dominates. If youve chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming. 数据占主导。如果您选择了正确的数据结构并组织得当，那么这些算法几乎总是不言而喻的。数据结构而非算法是编程的核心。 瑞士计算机科学家，Algol W，Modula，Oberon 和 Pascal 语言的设计师 Niklaus Emil Wirth 写过一本非常经典的书《Algorithms + Data Structures = Programs》，即算法 + 数据结构 = 程序。 我们可以得出结论，数据结构与算法之间是相辅相成的关系。数据结构服务于算法，算法作用于特定的数据结构之上。 数据结构与算法好难，怎么学？

几种排序算法分析

《几种排序算法的分析》 摘要: 排序算法是在C++中经常要用到的一种重要的算法。如何进行排序，特别是高效率的排序是是计算机应用中的一个重要课题。同一个问题可以构造不同的算法，最终选择哪一个好呢？这涉及如何评价一个算法好坏的问题，算法分析就是评估算法所消耗资源的方法。可以对同一问题的不同算法的代价加以比较，也可以由算法设计者根据算法分析判断一种算法在实现时是否会遇到资源限制的问题。排序的目的之一就是方便数据的查找。在实际生活中，应根据具体情况悬着适当的算法。一般的，对于反复使用的程序，应选取时间短的算法；对于涉及数据量较大，存储空间较小的情况则应选取节约存储空间的算法。本论文重点讨论时间复杂度。时间复杂度就是一个算法所消耗的时间。算法的效率指的是最坏情况下的算法效率。 排序分为内部排序和外部排序。本课程结业论文就内部排序算法（插入排序，选择排序，交换排序，归并排序和基数排序）的基本思想，排序步骤和实现算法等进行介绍。 本论文以较为详细的文字说明，表格对比，例子阐述等方面加以比较和总结，通过在参加数据的规模，记录说带的信息量大小，对排序稳定的要求，关键字的分布情况以及算法的时间复杂度和空间复杂度等方面进行比较，得出它们的优缺点和不足，从而加深了对它们的认识和了解，进而使自己在以后的学习和应用中能够更好的运用。

1.五种排序算法的实例： 1.1.插入排序 1.1.1.直接插入排序 思路：将数组分为无序区和有序区两个区，然后不断将无序区的第一个元素按大小顺序插入到有序区中去，最终将所有无序区元素都移动到有序区完成排序。 要点：设立哨兵，作为临时存储和判断数组边界之用。 实现： Void InsertSort(Node L[],int length) { Int i,j;//分别为有序区和无序区指针 for(i=1;i=1)//直到增量缩小为1 { Shell(L,d); d=d/2;//缩小增量 } } Void Shell(Node L[],int d) {

特色课程简介

明亮幼儿园园本特色课程简介 ——浅谈肢体律动在孩子学习成长中的作用奥尔夫音乐教育体系是当今世界最著名、影响最广的音乐教育体系之一。在奥尔夫音乐课堂中，它用儿童最喜欢的方式，比如在说儿歌、拍手、做游戏、唱歌等教学活动中，按韵律、节拍描摹事物形态、动作特点等做动作，培养儿童的节奏感和记忆力，使儿童通过感受韵律、节拍引导动作带来学习的兴趣。我校从08春开始尝试推广奥尔夫音乐教育体系——把肢体律动融入在课堂教学活动中。通过快乐大天使林永哲教授的音乐律动培训，结合我园实际课堂教学情况，在语言、健康、艺术、英语等课程的学习中运用最广泛的就是肢体律动。通过律动在各个课程领域的拓展，在日常的每节课的学习中使右脑潜能得到最大限度的开发。 “左右脑分工理论”认为：人的左脑从事逻辑思维，右脑从事形象思维，右脑是创造力的源泉，是艺术和经验学习的中枢，右脑的存储量是左脑的100万倍，可是在现实生活中，95%的人只使用了自己的左脑，科学家们指出，大多数人终其一生；只运用了大脑潜能的3%——4%，其余的97%都蕴藏在右脑的潜意识之中，这是一个多么令人吃惊和遗憾的事实。由于右脑具有瞬间接受大量刺激的能力，加以训练，不仅可以开发相当一部分潜能，更可促使大脑神经发达，扩大脑容量，进而有助于左脑的发育。肢体律动在日常教学活动过程即起到了刺激右脑潜能发展的作用。 一个6岁以下的孩子，他的动作发展已经成熟，听觉也已成熟，根据奥尔夫音乐课堂理论，我园利用肢体律动可以培养幼儿想象力、创造力，激发幼儿的学习兴趣，充分调动幼儿参与课堂的积极性，加深幼儿对所学内容的理解、记忆，增强幼儿自信心的特点，从而有效提高教学质量，使孩子终身受益。 下面我从四个方面浅谈肢体律动在教学活动中的作用 一、利用肢体律动，培养幼儿的想象力。 因为孩子天性好动，在课堂教学活动中有大量机会让幼儿去动、去玩、去

1998-Data mining-- statistics and more

Data Mining:Statistics and More? David J.H AND Data mining is a new discipline lying at the interface of statistics,database technology,pattern recognition,machine learning,and other areas.It is concerned with the secondary analysis of large databases in order to?nd previously un-suspected relationships which are of interest or value to the database owners.New problems arise,partly as a con-sequence of the sheer size of the data sets involved,and partly because of issues of pattern matching.However,since statistics provides the intellectual glue underlying the e?ort, it is important for statisticians to become involved.There are very real opportunities for statisticians to make signi?-cant contributions. KEY WORDS:Databases;Exploratory data analysis; Knowledge discovery. 1.DEFINITION AND OBJECTIVES The term data mining is not new to statisticians.It is a term synonymous with data dredging or?shing and has been used to describe the process of trawling through data in the hope of identifying patterns.It has a derogatory con-notation because a su?ciently exhaustive search will cer-tainly throw up patterns of some kind—by de?nition data that are not simply uniform have di?erences which can be interpreted as patterns.The trouble is that many of these “patterns”will simply be a product of random?uctuations, and will not represent any underlying structure.The object of data analysis is not to model the?eeting random pat-terns of the moment,but to model the underlying structures which give rise to consistent and replicable patterns.To statisticians,then,the term data mining conveys the sense of naive hope vainly struggling against the cold realities of chance. To other researchers,however,the term is seen in a much more positive light.Stimulated by progress in computer technology and electronic data acquisition,recent decades have seen the growth of huge databases,in?elds ranging from supermarket sales and banking,through astronomy, particle physics,chemistry,and medicine,to o?cial and governmental statistics.These databases are viewed as a re-source.It is certain that there is much valuable information in them,information that has not been tapped,and data min-ing is regarded as providing a set of tools by which that in-formation may be extracted.Looked at in this positive light, it is hardly surprising that the commercial,industrial,and David J.Hand is Professor of Statistics,Department of Statistics,The Open University,Milton Keynes,MK76AA,United Kingdom(E-mail: d.j.hand@https://www.sodocs.net/doc/267569568.html,).economic possibilities inherent in the notion of extracting information from these large masses of data have attracted considerable interest.The interest in the?eld is demon-strated by the fact that the Third International Conference on Knowledge Discovery and Data Mining,held in1997, attracted around700participants. Super?cially,of course,what we are describing here is nothing but exploratory data analysis,an activity which has been carried out since data were?rst analyzed and which achieved greater respectability through the work of John Tukey.But there is a di?erence,and it is this di?erence that explains why statisticians have been slow to latch on to the opportunities.This di?erence is the sheer size of the data sets now available.Statisticians have typically not con-cerned themselves with data sets containing many millions or even billions of records.Moreover,special storage and manipulation techniques are required to handle data collec-tions of this size—and the database technology which has grown up to handle them has been developed by entirely di?erent intellectual communities from statisticians. It is probably no exaggeration to say that most statis-ticians are concerned with primary data analysis.That is, the data are collected with a particular question or set of questions in mind.Indeed,entire subdisciplines,such as ex-perimental design and survey design,have grown up to fa-cilitate the e?cient collection of data so as to answer the given questions.Data mining,on the other hand,is entirely concerned with secondary data analysis.In fact we might de?ne data mining as the process of secondary analysis of large databases aimed at?nding unsuspected relationships which are of interest or value to the database owners.We see from this that data mining is very much an inductive exercise,as opposed to the hypothetico-deductive approach often seen as the paradigm for how modern science pro-gresses(Hand in press). 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