《人工智能與數(shù)據(jù)挖掘教學(xué)課件》lect113

1、Part IData Mining FundamentalsChapter 1: Data Mining: A First View第1頁，共39頁。2022/8/3BUPT AI&DM2Content1.1 What is Data Mining? Definition1.2 What can computers Learn?1.3 Is Data Mining Appropriate for My Problem?1.4 Expert Systems or Data Mining?1.6 Why Not Simple Search?第2頁，共39頁。2022/8/3BUPT AI&DM31

2、.1 What is data mining: MotivationData explosion problem Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories. Such amount of data beyond human understanding. We are drowning in data,

3、 but starving for knowledge! Solution: Data warehousing and data miningData warehousing: for data storageData mining: for Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases第3頁，共39頁。2022/8/3BUPT AI&DM41.1 Data Mining is a result of natural ev

4、olution of information technology1960s:Data collection and database creation1970s - early 1980s: Database Management SystemsMid-1980s - present:Data warehouseData analysis and understanding (data mining)第4頁，共39頁。2022/8/3BUPT AI&DM5Data Analysis：New TrendThis is a time that one must speak with data.未

5、來屬于運算師 (Super Crunchers超級運算師, Ian Ayres, 2009)：日常決策將變得越來越自動化，人的判斷作用將局限于為計算提供數(shù)據(jù)葡萄酒味道和香味的預(yù)測:奧利.阿申費爾特是普林斯頓大學(xué)的經(jīng)濟學(xué)家,完全不懂葡萄酒的制作，但可以預(yù)測波爾多葡萄酒的價格基于天氣（炎熱、干燥的年份酒會非常好），準(zhǔn)確率高于葡萄酒專家本書原計劃叫“理論的終結(jié)”，后來利用google改書名而不是與出版社編輯討論，因為發(fā)現(xiàn)用此名點擊率高63%放貸員曾經(jīng)收入優(yōu)厚、職責(zé)最大，現(xiàn)在只是呼叫中心的接線員，重復(fù)電腦提示的問題，報酬很低第5頁，共39頁。2022/8/3BUPT AI&DM6Data Analys

6、is：New Trend (cont.)This is a time that one must speak with data.基因測序和新物種：克雷格.文特爾使用能夠分析數(shù)據(jù)的高速計算機，從給單個生物基因排序，2003年開始給海洋測序，2005年給空氣測序。這個過程中發(fā)現(xiàn)了數(shù)千種以前不知道的細(xì)菌和其它生命形式。他對生物學(xué)的推進(jìn)比同輩所有人都大。第6頁，共39頁。2022/8/3BUPT AI&DM7在過去，上海通用保修問題分析主要依靠簡單的純手工處理的計算方式，每次只能產(chǎn)生寥寥幾篇問題報告。盡管汽車生產(chǎn)量遠(yuǎn)不如現(xiàn)在大，但這個耗時費力的分析周期卻在根本上導(dǎo)致了保修成本居高不下。在非自動操作環(huán)

7、境下，從保修索賠出現(xiàn)到找出問題原因平均要花費612個月的時間，且在此間往往還需要借助于通用全球的支持，解決問題的整個過程也主要建立在經(jīng)驗分析的基礎(chǔ)上。另外，不準(zhǔn)確的數(shù)據(jù)導(dǎo)致上海通用難以準(zhǔn)確預(yù)測保修成本，從而合理準(zhǔn)備下一周期的保修預(yù)算，導(dǎo)致大量運營資金被占用、現(xiàn)金流降低。 采用SAS的保修分析解決方案后，上海通用的保修分析周期在頭6個月里就縮短了70%，有效地降低了保修成本，實現(xiàn)了該系統(tǒng)使用的預(yù)期目標(biāo)。同時，這些顯著的改善效果幫助上海通用在短短半年內(nèi)就收回了保修分析系統(tǒng)所有的軟硬件投資，共為公司節(jié)省了1,800萬人民幣的成本。 警察地理信息系統(tǒng)第7頁，共39頁。2022/8/3BUPT AI&D

8、M8Data Mining Definitions(1) The process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data. (in this text book)(2) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from da

9、ta in large databases. (generally accepted)第8頁，共39頁。2022/8/3BUPT AI&DM9Induction-based Learning(基于歸納的學(xué)習(xí))Data mining methods use induction-based learningThe process of forming general concept definitions by observing specific examples of concepts to be learned.第9頁，共39頁。2022/8/3BUPT AI&DM10 What Is Da

10、ta Mining?Alternative names: Data mining or knowledge mining? Gold mining - poor analogyKnowledge discovery in databases (KDD), business intelligence第10頁，共39頁。2022/8/3BUPT AI&DM11Why Data Mining? Potential Applications (or p4)Database analysis and decision supportMarket analysis and managementtarget

11、 marketing, cross selling, market segmentationRisk analysis and managementForecasting, customer retention, quality controlFraud detection and managementOther ApplicationsText mining (news group, email, documents) and Web analysis.第11頁，共39頁。2022/8/3BUPT AI&DM12Content1.1 What is Data Mining? Definiti

12、on1.2 What can computers Learn?Four Levels of Learning（略）Three Concept Views （略）Supervised LearningUnsupervised Learning1.3 Is Data Mining Appropriate for My Problem?1.4 Expert Systems or Data Mining?1.6 Why Not Simple Search?第12頁，共39頁。2022/8/3BUPT AI&DM131.2.1 Supervised Learning Build a learner mo

13、del using data instances of known origin. Use the model to determine the outcome of new instances of unknown origin.第13頁，共39頁。2022/8/3BUPT AI&DM14Attributes: input attributes, output attributesProcess: Training Data ,Test DataLearning outcome: tree, production rules第14頁，共39頁。2022/8/3BUPT AI&DM15第15頁

14、，共39頁。2022/8/3BUPT AI&DM16Decision tree: A tree structure where non-terminal nodes represent tests on one or more attributes and terminal nodes (leaf nodes) reflect decision outcomes.root node第16頁，共39頁。2022/8/3BUPT AI&DM17Production Rules (產(chǎn)生式規(guī)則)IF Swollen Glands = Yes THEN Diagnosis = Strep ThroatI

15、F Swollen Glands = No & Fever = Yes THEN Diagnosis = ColdIF Swollen Glands = No & Fever = No THEN Diagnosis = AllergyAntecedent conditions: 先決條件Consequent conditions：結(jié)論第17頁，共39頁。2022/8/3BUPT AI&DM181.2.2 Unsupervised ClusteringA data mining method that builds models from data without predefined clas

16、ses. 第18頁，共39頁。2022/8/3BUPT AI&DM19The Acme Investors Dataset第19頁，共39頁。The Acme Investors Dataset & Supervised Learning Can I develop a general profile of an online investor?Can I determine if a new customer is likely to open a margin account?Can I build a model to accurately predict the average num

17、ber of trades per month for a new investor?What characteristics differentiate female and male investors?第20頁，共39頁。What attribute similarities group customers of Acme Investors together?What differences in attribute values segment the customer database? The Acme Investors Dataset & Unsupervised Clust

18、ering第21頁，共39頁。2022/8/3BUPT AI&DM22IF Margin Account = Yes & Age=20-29 & Annual Income = 40-59kTHEN Cluster = 1accuracy=0.80, coverage=0.50IF Account Type = Custodial & Favorite Recreation = Skiing & Annual Income = 80-90kTHEN Cluster = 2accuracy=0.95, coverage=0.35IF Account Type = Joint & Trades/M

19、onth 5 & Transaction Method = OnlineTHEN Cluster = 3accuracy=0.82, coverage=0.65(see example clusters on p13)第22頁，共39頁。2022/8/3BUPT AI&DM23Content1.1 What is Data Mining? Definition1.2 What can computers Learn?1.3 Is Data Mining Appropriate for My Problem? (Data Mining vs Data Query)1.4 Expert Syste

20、ms or Data Mining?1.6 Why Not Simple Search?第23頁，共39頁。2022/8/3BUPT AI&DM24Data Mining or Data Query? Shallow Knowledge: Shallow knowledge is factual. It can be easily stored and manipulated in a database. Multidimensional Knowledge: Multidimensional knowledge is also factual. On-line analytical Proc

21、essing (OLAP) tools are used to manipulate multidimensional knowledge. Hidden Knowledge: Hidden knowledge represents patterns or regularities in data that cannot be easily found using database query. However, data mining algorithms can find such patterns with ease (example p15). Deep Knowledge: Deep

22、 knowledge is knowledge stored in a database that can only be found if we are given some direction about what we are looking for. 第24頁，共39頁。Data Mining vs. Data Query: An Example (p16) Use data query if you already almost know what you are looking for. Use data mining to find regularities in data th

23、at are not obvious. 第25頁，共39頁。2022/8/3BUPT AI&DM26Content1.1 What is Data Mining? Definition1.2 What can computers Learn?1.3 Is Data Mining Appropriate for My Problem? (Data Mining vs Data Query)1.4 Expert Systems or Data Mining? (Data Mining vs ES)1.6 Why Not Simple Search?第26頁，共39頁。2022/8/3BUPT AI

24、&DM271.4 Expert Systems or Data Mining?Expert System (ES): A computer program that emulates the problem-solving skills of one or more human experts. Used when no (quality) data available, or, in the field where human has good knowledge in it.Experts learn their skills by education and experience.Hum

25、an experts often use rules to describe what they know.ES and DM can work together.第27頁，共39頁。2022/8/3BUPT AI&DM28第28頁，共39頁。2022/8/3BUPT AI&DM29Content1.1 What is Data Mining? Definition1.2 What can computers Learn?1.3 Is Data Mining Appropriate for My Problem? (Data Mining vs Data Query)1.4 Expert Sy

26、stems or Data Mining? (Data Mining vs ES)1.6 Why Not Simple Search? (Data Mining vs Nearest Neighbor Approach)第29頁，共39頁。2022/8/3BUPT AI&DM301.6 Why Not Simple Search?Stores instances or generalized model of the data. Nearest Neighbor ClassifierClassification is performed by searching the training data for the instance closest in distance to the unknown instance.Advantage: suitable for areas where human has limited knowledgeProblem: Slow when number of cases is largeAttribu