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A very short overview for Data Mining

Associate Professor

A very short overview for Data Mining

- 1. Quick Tour of Data Mining Yi-Shin Chen Institute of Information Systems and Applications Department of Computer Science National Tsing Hua University yishin@gmail.com
- 2. About Speaker 陳宜欣 Yi-Shin Chen ▷ Currently • 清華大學資訊工程系副教授 • 主持智慧型資料工程與應用實驗室 (IDEA Lab) ▷ Education • Ph.D. in Computer Science, USC, USA • M.B.A. in Information Management, NCU, TW • B.B.A. in Information Management, NCU, TW ▷ Courses (all in English) • Research and Presentation Skills • Introduction to Database Systems • Advanced Database Systems • Data Mining: Concepts, Techniques, and Applications 2
- 3. Evolution of Data Management The relationships between the techniques and our world 3
- 4. 4 1900 1920 1940 1950 1960 1970 Manual Record Managers 1950: Univac had developed a magnetic tape 1951: Univac I delivered to the US Census Bureau 1931: Gödel's Incompleteness Theorem 1948: Information theory (by Shannon) Information Entropy 1944: Mark I (Server) 1963: The origins of the Internet Programmed Record Managers • Birth of high-level programming languages • Batch processing Punched-Card Record Managers On-line Network Databases • Indexed sequential records • Data independence • Concurrent Access
- 5. 2001: Data Science 2009: Deep Learning 5 1970 1980 1990 2000 2010 1985: 1st standardized of SQL 1976: E-R Model by Peter Chen 1993: WWW 2006: Amazon.com Elastic Compute Cloud 1980: Artificial Neural Networks Knowledge Discovery in Databases Object Relational Model • Support multiple datatypes and applications 1974: IBM System R Relational Model • Give Database users high-level set-oriented data access operations
- 6. Data Mining What we know, and what we do now 6
- 7. Data Mining ▷ What is data mining? • Algorithms for seeking unexpected “pearls of wisdom” ▷ Current data mining research: • Focus on efficient ways to discover models of existing data sets • Developed algorithms are: classification, clustering, association- rule discovery, summarization…etc. 7
- 8. Data Mining Examples 8Slide from: Prof. Shou-De Lin
- 9. Origins of Data Mining ▷ Draws ideas from • Machine learning/AI • Pattern recognition • Statistics • Database systems ▷ Traditional Techniques may be unsuitable due to • Enormity of data • High dimensionality of data • Heterogeneous, distributed nature of data 9 ©Tan, Steinbach, Kumar Introduction to Data Mining Data Mining Machine Learning/AI Pattern Recognition Statistics Database
- 10. Knowledge Discovery (KDD) Process 10 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
- 11. Database 11
- 12. Database 12
- 13. Database 13
- 14. Informal Design Guidelines for Database ▷ Design a schema that can be explained easily relation by relation. The semantics of attributes should be easy to interpret ▷ Should avoid update anomaly problems ▷ Relations should be designed such that their tuples will have as few NULL values as possible ▷ The relations should be designed to satisfy the lossless join condition (guarantee meaningful results for join operations) 14
- 15. Data Warehouse ▷Assemble and manage data from various sources for the purpose of answering business questions 15 CRM ERP POS …OLTP Data Warehouse Meaningful
- 16. Knowledge Discovery (KDD) Process 16 Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
- 17. KDD Process: Several Key Steps ▷ Pre-processing • Learning the application domain → Relevant prior knowledge and goals of application • Creating a target data set: data selection • Data cleaning and preprocessing: (may take 60% of effort!) • Data reduction and transformation → Find useful features ▷ Data mining • Choosing functions of data mining → Choosing the mining algorithm • Search for patterns of interest ▷ Evaluation • Pattern evaluation and knowledge presentation → visualization, transformation, removing redundant patterns, etc. 17 ©Han & Kamper Data Mining: Concepts and Techniques
- 18. Data Many slides provided by Tan, Steinbach, Kumar for book “Introduction to Data Mining” are adapted in this presentation The most important part in the whole process 18
- 19. Types of Attributes ▷There are different types of attributes • Nominal (=,≠) → Nominal values can only distinguish one object from another → Examples: ID numbers, eye color, zip codes • Ordinal (<,>) → Ordinal values can help to order objects → Examples: rankings, grades • Interval (+,-) → The difference between values are meaningful → Examples: calendar dates • Ratio (*,/) → Both differences and ratios are meaningful → Examples: temperature in Kelvin, length, time, counts 19只有這一種能適用所有的處理方法
- 20. Types of Data Sets ▷Record • Data Matrix • Document Data • Transaction Data ▷Graph • World Wide Web • Molecular Structures ▷Ordered • Spatial Data • Temporal Data • Sequential Data • Genetic Sequence Data 20 1.12.216.226.2512.65 1.22.715.225.2710.23 ThicknessLoadDistanceProjection of y load Projection of x Load 1.12.216.226.2512.65 1.22.715.225.2710.23 ThicknessLoadDistanceProjection of y load Projection of x Load Document 1 season timeout lost wi n game score ball pla y coach team Document 2 Document 3 3 0 5 0 2 6 0 2 0 2 0 0 7 0 2 1 0 0 3 0 0 1 0 0 1 2 2 0 3 0 TID Time Items 1 2009/2/8 Bread, Coke, Milk 2 2009/2/13 Beer, Bread 3 2009/2/23 Beer, Diaper 4 2009/3/1 Coke, Diaper, Milk
- 21. A Facebook Example 21
- 22. Data Matrix/Graph Data Example 22
- 23. Document Data 23
- 24. Transaction Data 24
- 25. Spatio-Temporal Data 25
- 26. Sequential Data 26 2017/1/3 2016/12/31 2016/12/31 2016/11/28 2016/11/17 2016/11/09 2016/11/08 2016/11/08 2016/11/08 2016/11/08 2016/11/05
- 27. Tips for Converting Text to Numerical Values 27
- 28. Recap: Types of Attributes ▷There are different types of attributes • Nominal (=,≠) → Nominal values can only distinguish one object from another → Examples: ID numbers, eye color, zip codes • Ordinal (<,>) → Ordinal values can help to order objects → Examples: rankings, grades • Interval (+,-) → The difference between values are meaningful → Examples: calendar dates • Ratio (*,/) → Both differences and ratios are meaningful → Examples: temperature in Kelvin, length, time, counts 28
- 29. Vector Space Model ▷Represent the keywords of objects using a term vector • Term: basic concept, e.g., keywords to describe an object • Each term represents one dimension in a vector • N total terms define an n-element terms • Values of each term in a vector corresponds to the importance of that term ▷Measure similarity by the vector distances 29 Document 1 season timeout lost wi n game score ball pla y coach team Document 2 Document 3 3 0 5 0 2 6 0 2 0 2 0 0 7 0 2 1 0 0 3 0 0 1 0 0 1 2 2 0 3 0
- 30. Term Frequency and Inverse Document Frequency (TFIDF) ▷Since not all objects in the vector space are equally important, we can weight each term using its occurrence probability in the object description • Term frequency: TF(d,t) → number of times t occurs in the object description d • Inverse document frequency: IDF(t) → to scale down the terms that occur in many descriptions 30
- 31. Normalizing Term Frequency ▷nij represents the number of times a term ti occurs in a description dj . tfij can be normalized using the total number of terms in the document • 𝑡𝑓𝑖𝑗 = 𝑛 𝑖𝑗 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑𝑉𝑎𝑙𝑢𝑒 ▷NormalizedValue could be: • Sum of all frequencies of terms • Max frequency value • Any other values can make tfij between 0 to 1 31
- 32. Inverse Document Frequency ▷ IDF seeks to scale down the coordinates of terms that occur in many object descriptions • For example, some stop words(the, a, of, to, and…) may occur many times in a description. However, they should be considered as non-important in many cases • 𝑖𝑑𝑓𝑖 = 𝑙𝑜𝑔 𝑁 𝑑𝑓 𝑖 + 1 → where dfi (document frequency of term ti) is the number of descriptions in which ti occurs ▷ IDF can be replaced with ICF (inverse class frequency) and many other concepts based on applications 32
- 33. Reasons of Log ▷ Each distribution can indicate the hidden force • • • 33 Power-law distribution Normal distribution Normal distribution
- 34. Data Quality Dirty Data 34
- 35. Big Data? ▷ “Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is “big data.” • --from www.ibm.com/software/data/bigdata/what-is-big-data.html 35
- 36. 4V 36
- 37. Data Quality ▷What kinds of data quality problems? ▷How can we detect problems with the data? ▷What can we do about these problems? ▷Examples of data quality problems: • Noise and outliers • Missing values • Duplicate data 37
- 38. Noise ▷Noise refers to modification of original values • Examples: distortion of a person’s voice when talking on a poor phone and “snow” on television screen 38Two Sine Waves Two Sine Waves + Noise
- 39. Outliers ▷Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set 39
- 40. Missing Values ▷Reasons for missing values • Information is not collected → e.g., people decline to give their age and weight • Attributes may not be applicable to all cases → e.g., annual income is not applicable to children ▷Handling missing values • Eliminate Data Objects • Estimate Missing Values • Ignore the Missing Value During Analysis • Replace with all possible values → Weighted by their probabilities 40
- 41. Duplicate Data ▷Data set may include data objects that are duplicates, or almost duplicates of one another • Major issue when merging data from heterogeneous sources ▷Examples: • Same person with multiple email addresses ▷Data cleaning • Process of dealing with duplicate data issues 41
- 42. Data Preprocessing To be or not to be 42
- 43. Data Preprocessing ▷Aggregation ▷Sampling ▷Dimensionality reduction ▷Feature subset selection ▷Feature creation ▷Discretization and binarization ▷Attribute transformation 43
- 44. Aggregation ▷Combining two or more attributes (or objects) into a single attribute (or object) ▷Purpose • Data reduction → Reduce the number of attributes or objects • Change of scale → Cities aggregated into regions, states, countries, etc • More “stable” data → Aggregated data tends to have less variability 44 SELECT d.Name, avg(Salary) FROM Employee AS e, Department AS d WHERE e.Dept=d.DNo GROUP BY d.Name HAVING COUNT(e.ID)>=2;
- 45. Sampling ▷Sampling is the main technique employed for data selection • It is often used for both → Preliminary investigation of the data → The final data analysis • Reasons: → Statistics: Obtaining the entire set of data of interest is too expensive → Data mining: Processing the entire data set is too expensive 45
- 46. Key Principle For Effective Sampling ▷The sample is representative • Using a sample will work almost as well as using the entire data sets • The approximately the same property as the original set of data 46
- 47. Sample Size Matters 47 8000 points 2000 Points 500 Points
- 48. Sampling Bias ▷ 2004 Taiwan presidential election polls 48 TVBS 聯合報 訪問日期 93 年 1 月 15日 至 1 月 17日 有效樣本 1068 人 拒 訪 699 人 抽樣誤差 在 95% 信心水準下，約 ± 3個百分點 訪問地區 台灣地區 抽樣方法 電話簿分層系統抽樣，電話號碼末二位隨機
- 49. Dimensionality Reduction ▷Purpose: • Avoid curse of dimensionality • Reduce amount of time and memory required by data mining algorithms • Allow data to be more easily visualized • May help to eliminate irrelevant features or reduce noise ▷Techniques • Principle Component Analysis • Singular Value Decomposition • Others: supervised and non-linear techniques 49
- 50. Curse of Dimensionality ▷When dimensionality increases, data becomes increasingly sparse in the space that it occupies • Definitions of density and distance between points, which is critical for clustering and outlier detection, become less meaningful 50 • Randomly generate 500 points • Compute difference between max and min distance between any pair of points
- 51. Dimensionality Reduction: PCA ▷Goal is to find a projection that captures the largest amount of variation in data 51 x2 x1 e
- 52. Feature Subset Selection ▷Another way to reduce dimensionality of data ▷Redundant features • Duplicate much or all of the information contained in one or more other attributes • E.g. purchase price of a product vs. sales tax ▷Irrelevant features • Contain no information that is useful for the data mining task at hand • E.g. students' ID is often irrelevant to the task of predicting students' GPA 52
- 53. Feature Creation ▷Create new attributes that can capture the important information in a data set much more efficiently than the original attributes ▷Three general methodologies: • Feature extraction →Domain-specific • Mapping data to new space • Feature construction →Combining features 53
- 54. Mapping Data to a New Space ▷Fourier transform ▷Wavelet transform 54 Two Sine Waves Two Sine Waves + Noise Frequency
- 55. Discretization Using Class Labels ▷Entropy based approach 55 3 categories for both x and y 5 categories for both x and y
- 56. Discretization Without Using Class Labels 56
- 57. Attribute Transformation ▷A function that maps the entire set of values of a given attribute to a new set of replacement values • So each old value can be identified with one of the new values • Simple functions: xk, log(x), ex, |x| • Standardization and Normalization 57
- 58. Transformation Examples 58 Log(Frequency) Log(Log(Frequency))
- 59. Preprocessing in Reality 59
- 60. Data Collection ▷Align /Classify the attributes correctly 60 Who post this message Mentioned User Hashtag Shared URL
- 61. Language Detection ▷To detect an language (possible languages) in which the specified text is written ▷Difficulties • Short message • Different languages in one statement • Noisy 61 你好 現在幾點鐘 apa kabar sekarang jam berapa ? 繁體中文 (zh-tw) 印尼文 (id)
- 62. Wrong Detection Examples ▷Twitter examples 62 @sayidatynet top song #LailaGhofran shokran ya garh new album #listen 中華隊的服裝挺特別的，好藍。。。 #ChineseTaipei #Sochi #2014冬奧 授業前の雪合戦w http://t.co/d9b5peaq7J Before / after removing noise en -> id it -> zh-tw en -> ja
- 63. Removing Noise ▷Removing noise before detection • Html file ->tags • Twitter -> hashtag, mention, URL 63 <meta name="twitter:description" content="觸犯法國隱私法〔駐歐洲特派記 者胡蕙寧、國際新聞中心／綜合報導〕網路 搜 尋 引 擎 巨 擘 Google8 日 在 法 文 版 首 頁 （www.google.fr）張貼悔過書 ..."/> 觸犯法國隱私法〔駐歐洲特派記者胡蕙寧、國際新聞中 心／綜合報導〕網路搜尋引擎巨擘Google8日在法文版 首頁（www.google.fr）張貼悔過書 ... 英文 (en) 繁中 (zh-tw)
- 64. Data Cleaning ▷Special character ▷Utilize regular expressions to clean data 64 Unicode emotions ☺, ♥… Symbol icon ☏, ✉… Currency symbol €, £, $... Tweet URL Filter out non-(letters, space, punctuation, digit) ◕‿◕ Friendship is everything ♥ ✉ xxxx@gmail.com I added a video to a @YouTube playlist http://t.co/ceYX62StGO Jamie Riepe (^|s*)http(S+)?(s*|$) (p{L}+)|(p{Z}+)| (p{Punct}+)|(p{Digit}+)
- 65. Japanese Examples ▷Use regular expression remove all special words • うふふふふ(*^^*)楽しむ！ありがとうございま す^o^ アイコン、ラブラブ(-_-)♡ • うふふふふ 楽しむ ありがとうございます ア イコン ラブラブ 65 W
- 66. Part-of-speech (POS) Tagging ▷Processing text and assigning parts of speech to each word ▷Twitter POS tagging • Noun (N), Adjective (A), Verb (V), URL (U)… 66 Happy Easter! I went to work and came home to an empty house now im going for a quick run http://t.co/Ynp0uFp6oZ Happy_A Easter_N !_, I_O went_V to_P work_N and_& came_V home_N to_P an_D empty_A house_N now_R im_L going_V for_P a_D quick_A run_N http://t.co/Ynp0uFp6oZ_U
- 67. Stemming ▷@DirtyDTran gotta be caught up for tomorrow nights episode ▷@ASVP_Jaykey for some reasons I found this very amusing 67 • @DirtyDTran gotta be catch up for tomorrow night episode • @ASVP_Jaykey for some reason I find this very amusing RT @kt_biv : @caycelynnn loving and missing you! we are still looking for Lucy love miss be look
- 68. Hashtag Segmentation ▷By using Microsoft Web N-Gram Service (or by using Viterbi algorithm) 68 #pray #for #boston Wow! explosion at a boston race ... #prayforboston #citizenscience #bostonmarathon #goodthingsarecoming #lowbloodpressure → → → → #citizen #science #boston #marathon #good #things #are #coming #low #blood #pressure
- 69. More Preprocesses for Different Web Data ▷Extract source code without javascript ▷Removing html tags 69
- 70. Extract Source Code Without Javascript ▷Javascript code should be considered as an exception • it may contain hidden content 70
- 71. Remove Html Tags ▷Removing html tags to extract meaningful content 71
- 72. More Preprocesses for Different Languages ▷Chinese Simplified/Traditional Conversion ▷Word segmentation 72
- 73. Chinese Simplified/Traditional Conversion ▷Word conversion • 请乘客从后门落车 → 請乘客從後門下車 ▷One-to-many mapping • @shinrei 出去旅游还是崩坏 → @shinrei 出去旅游還是崩壞 游 (zh-cn) → 游|遊 (zh-tw) ▷Wrong segmentation • 人体内存在很多微生物 → 內存: 人體 記憶體 在很多微生物 → 存在: 人體內 存在 很多微生物 73 內存|存在
- 74. Wrong Chinese Word Segmentation ▷Wrong segmentation • 這(Nep) 地面(Nc) 積(VJ) 還(D) 真(D) 不(D) 小(VH) http://t.co/QlUbiaz2Iz ▷Wrong word • @iamzeke 實驗(Na) 室友(Na) 多(Dfa) 危險(VH) 你(Nh) 不(D) 知道(VK) 嗎 (T) ? ▷Wrong order • 人體(Na) 存(VC) 內在(Na) 很多(Neqa) 微生物(Na) ▷Unknown word • 半夜(Nd) 逛團(Na) 購(VC) 看到(VE) 太(Dfa) 吸引人(VH) !! 74 地面|面積 實驗室|室友 存在|內在 未知詞: 團購
- 75. Similarity and Dissimilarity To like or not to like 75
- 76. Similarity and Dissimilarity ▷Similarity • Numerical measure of how alike two data objects are. • Is higher when objects are more alike. • Often falls in the range [0,1] ▷Dissimilarity • Numerical measure of how different are two data objects • Lower when objects are more alike • Minimum dissimilarity is often 0 • Upper limit varies 76
- 77. Euclidean Distance Where n is the number of dimensions (attributes) and pk and qk are, respectively, the kth attributes (components) or data objects p and q. ▷Standardization is necessary, if scales differ. 77 n k kk qpdist 1 2 )(
- 78. Minkowski Distance ▷ Minkowski Distance is a generalization of Euclidean Distance Where r is a parameter, n is the number of dimensions (attributes) and pk and qk are, respectively, the kth attributes (components) or data objects p and q. 78 r n k r kk qpdist 1 1 )||( : is extremely sensitive to the scales of the variables involved
- 79. Mahalanobis Distance ▷Mahalanobis distance measure: • Transforms the variables into covariance • Make the covariance equal to 1 • Calculate simple Euclidean distance 79 )()(),( 1 yxSyxyxd S is the covariance matrix of the input data
- 80. Similarity Between Binary Vectors ▷ Common situation is that objects, p and q, have only binary attributes ▷ Compute similarities using the following quantities M01 = the number of attributes where p was 0 and q was 1 M10 = the number of attributes where p was 1 and q was 0 M00 = the number of attributes where p was 0 and q was 0 M11 = the number of attributes where p was 1 and q was 1 ▷ Simple Matching and Jaccard Coefficients SMC = number of matches / number of attributes = (M11 + M00) / (M01 + M10 + M11 + M00) J = number of 11 matches / number of not-both-zero attributes values = (M11) / (M01 + M10 + M11) 80
- 81. Cosine Similarity ▷ If d1 and d2 are two document vectors, then cos( d1, d2 ) = (d1 d2) / ||d1|| ||d2|| , where indicates vector dot product and || d || is the length of vector d. ▷ Example: d1 = 3 2 0 5 0 0 0 2 0 0 d2 = 1 0 0 0 0 0 0 1 0 2 d1 d2= 3*1 + 2*0 + 0*0 + 5*0 + 0*0 + 0*0 + 0*0 + 2*1 + 0*0 + 0*2 = 5 ||d1|| = (3*3+2*2+0*0+5*5+0*0+0*0+0*0+2*2+0*0+0*0)0.5 = (42) 0.5 = 6.481 ||d2|| = (1*1+0*0+0*0+0*0+0*0+0*0+0*0+1*1+0*0+2*2) 0.5 = (6) 0.5 = 2.245 cos( d1, d2 ) = .3150 81
- 82. Correlation ▷ Correlation measures the linear relationship between objects ▷ To compute correlation, we standardize data objects, p and q, and then take their dot product 82 )(/))(( pstdpmeanpp kk )(/))(( qstdqmeanqq kk qpqpncorrelatio ),(
- 83. Using Weights to Combine Similarities ▷May not want to treat all attributes the same. • Use weights wk which are between 0 and 1 and sum to 1. 83
- 84. Density ▷Density-based clustering require a notion of density ▷Examples: • Euclidean density → Euclidean density = number of points per unit volume • Probability density • Graph-based density 84
- 85. Data Exploration Seeing is beliving
- 86. Data Exploration ▷A preliminary exploration of the data to better understand its characteristics ▷Key motivations of data exploration include • Helping to select the right tool for preprocessing or analysis • Making use of humans’ abilities to recognize patterns • People can recognize patterns not captured by data analysis tools 86
- 87. Summary Statistics ▷Summary statistics are numbers that summarize properties of the data • Summarized properties include frequency, location and spread → Examples: location - mean spread - standard deviation • Most summary statistics can be calculated in a single pass through the data 87
- 88. Frequency and Mode ▷Given a set of unordered categorical values → Compute the frequency with each value occurs is the easiest way ▷The mode of a categorical attribute • The attribute value that has the highest frequency 88 m v vfrequency i i valueattributewithovjectsofnumber
- 89. Percentiles ▷For ordered data, the notion of a percentile is more useful ▷Given • An ordinal or continuous attribute x • A number p between 0 and 100 ▷The pth percentile xp is a value of x • p% of the observed values of x are less than xp 89
- 90. Measures of Location: Mean and Median ▷The mean is the most common measure of the location of a set of points. • However, the mean is very sensitive to outliers. • Thus, the median or a trimmed mean is also commonly used 90
- 91. Measures of Spread: Range and Variance ▷Range is the difference between the max and min ▷The variance or standard deviation is the most common measure of the spread of a set of points. ▷However, this is also sensitive to outliers, so that other measures are often used 91
- 92. Visualization Visualization is the conversion of data into a visual or tabular format ▷Visualization of data is one of the most powerful and appealing techniques for data exploration. • Humans have a well developed ability to analyze large amounts of information that is presented visually • Can detect general patterns and trends • Can detect outliers and unusual patterns 92
- 93. Arrangement ▷Is the placement of visual elements within a display ▷Can make a large difference in how easy it is to understand the data ▷Example 93
- 94. Visualization Techniques: Histograms ▷ Histogram • Usually shows the distribution of values of a single variable • Divide the values into bins and show a bar plot of the number of objects in each bin. • The height of each bar indicates the number of objects • Shape of histogram depends on the number of bins ▷ Example: Petal Width (10 and 20 bins, respectively) 94
- 95. Visualization Techniques: Box Plots ▷Another way of displaying the distribution of data • Following figure shows the basic part of a box plot 95
- 96. Scatter Plot Array 96
- 97. Visualization Techniques: Contour Plots ▷Contour plots • Partition the plane into regions of similar values • The contour lines that form the boundaries of these regions connect points with equal values • The most common example is contour maps of elevation • Can also display temperature, rainfall, air pressure, etc. 97 Celsius Sea Surface Temperature (SST)
- 98. Visualization of the Iris Data Matrix 98 standard deviation
- 99. Visualization of the Iris Correlation Matrix 99
- 100. Visualization Techniques: Star Plots ▷ Similar approach to parallel coordinates • One axis for each attribute ▷ The size and the shape of polygon fives a visual description of the attribute value of the object 100 Petal length sepal length SepalwidthPetalwidth
- 101. Visualization Techniques: Chernoff Faces ▷This approach associates each attribute with a characteristic of a face ▷The values of each attribute determine the appearance of the corresponding facial characteristic ▷Each object becomes a separate face 101 Data Feature Facial Feature Sepal length Size of face Sepal width Forehead/jaw relative arc length Petal length Shape of forehead Petal width Shape of jaw
- 102. Do's and Don'ts ▷ Apprehension • Correctly perceive relations among variables ▷ Clarity • Visually distinguish all the elements of a graph ▷ Consistency • Interpret a graph based on similarity to previous graphs ▷ Efficiency • Portray a possibly complex relation in as simple a way as possible ▷ Necessity • The need for the graph, and the graphical elements ▷ Truthfulness • Determine the true value represented by any graphical element 102
- 103. Data Mining Techniques Yi-Shin Chen Institute of Information Systems and Applications Department of Computer Science National Tsing Hua University yishin@gmail.com Many slides provided by Tan, Steinbach, Kumar for book “Introduction to Data Mining” are adapted in this presentation
- 104. Overview Understand the objectivities 104
- 105. Tasks in Data Mining ▷Problems should be well defined at the beginning ▷Two categories of tasks [Fayyad et al., 1996] 105 Predictive Tasks • Predict unknown values • e.g., potential customers Descriptive Tasks • Find patterns to describe data • e.g., Friendship finding VIP Cheap Potential
- 106. Select Techniques ▷Problems could be further decomposed 106 Predictive Tasks • Classification • Ranking • Regression • … Descriptive Tasks • Clustering • Association rules • Summarization • … Supervised Learning Unsupervised Learning
- 107. Supervised vs. Unsupervised Learning ▷ Supervised learning • Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations • New data is classified based on the training set ▷ Unsupervised learning • The class labels of training data is unknown • Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data 107
- 108. Classification ▷ Given a collection of records (training set ) • Each record contains a set of attributes • One of the attributes is the class ▷ Find a model for class attribute: • The model forms a function of the values of other attributes ▷ Goal: previously unseen records should be assigned a class as accurately as possible. • A test set is needed → To determine the accuracy of the model ▷Usually, the given data set is divided into training & test • With training set used to build the model • With test set used to validate it 108
- 109. Ranking ▷Produce a permutation to items in a new list • Items ranked in higher positions should be more important • E.g., Rank webpages in a search engine Webpages in higher positions are more relevant. 109
- 110. Regression ▷Find a function which model the data with least error • The output might be a numerical value • E.g.: Predict the stock value 110
- 111. Clustering ▷Group data into clusters • Similar to the objects within the same cluster • Dissimilar to the objects in other clusters • No predefined classes (unsupervised classification) 111
- 112. Association Rule Mining ▷Basic concept • Given a set of transactions • Find rules that will predict the occurrence of an item • Based on the occurrences of other items in the transaction 112
- 113. Summarization ▷Provide a more compact representation of the data • Data: Visualization • Text – Document Summarization → E.g.: Snippet 113
- 114. Classification 114
- 115. Illustrating Classification Task 115 Apply Model Induction Deduction Learn Model Model Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes 10 Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K ? 12 Yes Medium 80K ? 13 Yes Large 110K ? 14 No Small 95K ? 15 No Large 67K ? 10 Test Set Learning algorithm Training Set
- 116. Decision Tree 116 Tid Refund Marital Status Taxable Income Cheat 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes 10 categorical continuous class ©Tan, Steinbach, Kumar Introduction to Data Mining Refund MarSt TaxInc YESNO NO NO Yes No MarriedSingle, Divorced < 80K > 80K Splitting Attributes Model: Decision TreeTraining Data There could be more than one tree that fits the same data!
- 117. Algorithm for Decision Tree Induction ▷ Basic algorithm (a greedy algorithm) • Tree is constructed in a top-down recursive divide-and-conquer manner • At start, all the training examples are at the root • Attributes are categorical (if continuous-valued, they are discretized in advance) • Examples are partitioned recursively based on selected attributes • Test attributes are selected on the basis of a heuristic or statistical measure (e.g., information gain) Data Mining 117
- 118. Tree Induction ▷Greedy strategy. • Split the records based on an attribute test that optimizes certain criterion. ▷Issues • Determine how to split the records → How to specify the attribute test condition? → How to determine the best split? • Determine when to stop splitting 118 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 119. The Problem Of Decision Tree 119 Deep Bushy Tree Deep Bushy Tree Useless The Decision Tree has a hard time with correlated attributes 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 ? 100 10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70 80 90
- 120. Advantages/Disadvantages of Decision Trees ▷Advantages: • Easy to understand • Easy to generate rules ▷Disadvantages: • May suffer from overfitting. • Classifies by rectangular partitioning (so does not handle correlated features very well). • Can be quite large – pruning is necessary. • Does not handle streaming data easily 120
- 121. Underfitting and Overfitting 121 Underfitting: when model is too simple, both training and test errors are large ©Tan, Steinbach, Kumar Introduction to Data Mining
- 122. Overfitting due to Noise 122 ©Tan, Steinbach, Kumar Introduction to Data Mining Decision boundary is distorted by noise point
- 123. Overfitting due to Insufficient Examples 123 ©Tan, Steinbach, Kumar Introduction to Data Mining Lack of data points in the lower half of the diagram makes it difficult to predict correctly the class labels of that region - Insufficient number of training records in the region causes the decision tree to predict the test examples using other training records that are irrelevant to the classification task
- 124. Bayes Classifier ▷A probabilistic framework for solving classification problems ▷Conditional Probability: ▷ Bayes theorem: 124 ©Tan, Steinbach, Kumar Introduction to Data Mining )( )()|( )|( AP CPCAP ACP )( ),( )|( )( ),( )|( CP CAP CAP AP CAP ACP
- 125. Bayesian Classifiers ▷Consider each attribute and class label as random variables ▷Given a record with attributes (A1, A2,…,An) • Goal is to predict class C • Specifically, we want to find the value of C that maximizes P(C| A1, A2,…,An ) ▷Can we estimate P(C| A1, A2,…,An ) directly from data? 125 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 126. Bayesian Classifier Approach ▷Compute the posterior probability P(C | A1, A2, …, An) for all values of C using the Bayes theorem ▷Choose value of C that maximizes P(C | A1, A2, …, An) ▷Equivalent to choosing value of C that maximizes P(A1, A2, …, An|C) P(C) ▷How to estimate P(A1, A2, …, An | C )? 126 ©Tan, Steinbach, Kumar Introduction to Data Mining )( )()|( )|( 21 21 21 n n n AAAP CPCAAAP AAACP
- 127. Naïve Bayes Classifier ▷A simplified assumption: attributes are conditionally independent and each data sample has n attributes ▷No dependence relation between attributes ▷By Bayes theorem, ▷As P(X) is constant for all classes, assign X to the class with maximum P(X|Ci)*P(Ci) 127 n k CixkPCiXP 1 )|()|( )( )()|()|( XP CiPCiXPXCiP
- 128. Naïve Bayesian Classifier: Comments ▷ Advantages : • Easy to implement • Good results obtained in most of the cases ▷ Disadvantages • Assumption: class conditional independence • Practically, dependencies exist among variables → E.g., hospitals: patients: Profile: age, family history etc → E.g., Symptoms: fever, cough etc., Disease: lung cancer, diabetes etc • Dependencies among these cannot be modeled by Naïve Bayesian Classifier ▷ How to deal with these dependencies? • Bayesian Belief Networks 128
- 129. Bayesian Networks ▷Bayesian belief network allows a subset of the variables conditionally independent ▷A graphical model of causal relationships • Represents dependency among the variables • Gives a specification of joint probability distribution Data Mining 129
- 130. Bayesian Belief Network: An Example 130 Family History LungCancer PositiveXRay Smoker Emphysema Dyspnea LC ~LC (FH, S) (FH, ~S) (~FH, S) (~FH, ~S) 0.8 0.2 0.5 0.5 0.7 0.3 0.1 0.9 Bayesian Belief Networks The conditional probability table for the variable LungCancer: Shows the conditional probability for each possible combination of its parents n i ZParents iziPznzP 1 ))(|(),...,1(
- 131. Neural Networks ▷Artificial neuron • Each input is multiplied by a weighting factor. • Output is 1 if sum of weighted inputs exceeds a threshold value; 0 otherwise ▷Network is programmed by adjusting weights using feedback from examples 131
- 132. General Structure Data Mining 132 Output nodes Input nodes Hidden nodes Output vector Input vector: xi wij i jiijj OwI jIj e O 1 1 ))(1( jjjjj OTOOErr jk k kjjj wErrOOErr )1( ijijij OErrlww )( jjj Errl)(
- 133. Network Training ▷The ultimate objective of training • Obtain a set of weights that makes almost all the tuples in the training data classified correctly ▷Steps • Initialize weights with random values • Feed the input tuples into the network one by one • For each unit → Compute the net input to the unit as a linear combination of all the inputs to the unit → Compute the output value using the activation function → Compute the error → Update the weights and the bias 133
- 134. Summary of Neural Networks ▷Advantages • Prediction accuracy is generally high • Robust, works when training examples contain errors • Fast evaluation of the learned target function ▷Criticism • Long training time • Difficult to understand the learned function (weights) • Not easy to incorporate domain knowledge 134
- 135. The k-Nearest Neighbor Algorithm ▷All instances correspond to points in the n-D space. ▷The nearest neighbor are defined in terms of Euclidean distance. ▷The target function could be discrete- or real- valued. ▷For discrete-valued, the k-NN returns the most common value among the k training examples nearest to xq. 135 . _ + _ xq + _ _ + _ _ +
- 136. Discussion on the k-NN Algorithm ▷Distance-weighted nearest neighbor algorithm • Weight the contribution of each of the k neighbors according to their distance to the query point xq → Giving greater weight to closer neighbors ▷Curse of dimensionality: distance between neighbors could be dominated by irrelevant attributes. • To overcome it, elimination of the least relevant attributes. 136
- 137. Association Rule Mining
- 138. Definition: Frequent Itemset ▷ Itemset: A collection of one or more items • Example: {Milk, Bread, Diaper} ▷ k-itemset • An itemset that contains k items ▷ Support count () • Frequency of occurrence of an itemset • E.g. ({Milk, Bread,Diaper}) = 2 ▷ Support • Fraction of transactions that contain an itemset • E.g. s({Milk, Bread, Diaper}) = 2/5 ▷ Frequent Itemset • An itemset whose support is greater than or equal to a minsup threshold 138 ©Tan, Steinbach, Kumar Introduction to Data Mining Market-Basket transactions TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 139. Definition: Association Rule 139 Association Rule – An implication expression of the form X Y, where X and Y are itemsets – Example: {Milk, Diaper} {Beer} Rule Evaluation Metrics – Support (s) Fraction of transactions that contain both X and Y – Confidence (c) Measures how often items in Y appear in transactions that contain X ©Tan, Steinbach, Kumar Introduction to Data Mining Market-Basket transactions Example: Beer}Diaper,Milk{ 4.0 5 2 |T| )BeerDiaper,,Milk( s 67.0 3 2 )Diaper,Milk( )BeerDiaper,Milk,( c TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 140. Strong Rules & Interesting 140 ▷Corr(A,B)=P(AUB)/(P(A)P(B)) • Corr(A, B)=1, A & B are independent • Corr(A, B)<1, occurrence of A is negatively correlated with B • Corr(A, B)>1, occurrence of A is positively correlated with B ▷E.g. Corr(games, videos)=0.4/(0.6*0.75)=0.89 • In fact, games & videos are negatively associated → Purchase of one actually decrease the likelihood of purchasing the other 10000 6000 games 7500 video4000
- 141. Clustering Analysis
- 142. Good Clustering ▷Good clustering (produce high quality clusters) • Intra-cluster similarity is high • Inter-cluster class similarity is low ▷Quality factors • Similarity measure and its implementation • Definition and representation of cluster chosen • Clustering algorithm 142
- 143. Types of Clusters: Well-Separated ▷Well-Separated clusters: • A cluster is a set of points such that any point in a cluster is closer (or more similar) to every other point in the cluster than to any point not in the cluster. 143 ©Tan, Steinbach, Kumar Introduction to Data Mining 3 well-separated clusters
- 144. Types of Clusters: Center-Based ▷Center-based • A cluster is a set of objects such that an object in a cluster is closer (more similar) to the “center” of a cluster • The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster 144 ©Tan, Steinbach, Kumar Introduction to Data Mining 4 center-based clusters
- 145. Types of Clusters: Contiguity-Based ▷Contiguous cluster (Nearest neighbor or transitive) • A cluster is a set of points such that a point in a cluster is closer (or more similar) to one or more other points in the cluster than to any point not in the cluster. 145 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 146. Types of Clusters: Density-Based ▷Density-based • A cluster is a dense region of points, which is separated by low-density regions, from other regions of high density. • Used when the clusters are irregular or intertwined, and when noise and outliers are present. 146 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 147. Types of Clusters: Objective Function ▷Clusters defined by an objective function • Finds clusters that minimize or maximize an objective function. • Naïve approaches: → Enumerate all possible ways → Evaluate the `goodness' of each potential set of clusters →NP Hard • Can have global or local objectives. → Hierarchical clustering algorithms typically have local objectives → Partitioned algorithms typically have global objectives 147 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 148. Partitioning Algorithms: Basic Concept ▷Given a k, find a partition of k clusters that optimizes the chosen partitioning criterion • Global optimal: exhaustively enumerate all partitions. • Heuristic methods. → k-means: each cluster is represented by the center of the cluster → k-medoids or PAM (Partition Around Medoids) : each cluster is represented by one of the objects in the cluster. 148
- 149. K-Means Clustering Algorithm ▷Algorithm: • Randomly initialize k cluster means • Iterate: → Assign each genes to the nearest cluster mean → Recompute cluster means • Stop when clustering converges 149 K=4
- 150. Two different K-means Clusterings 150 ©Tan, Steinbach, Kumar Introduction to Data Mining -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Sub-optimal Clustering -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Optimal Clustering Original Points
- 151. Solutions to Initial Centroids Problem ▷ Multiple runs • Helps, but probability is not on your side ▷ Sample and use hierarchical clustering to determine initial centroids ▷ Select more than k initial centroids and then select among these initial centroids • Select most widely separated ▷ Postprocessing ▷ Bisecting K-means • Not as susceptible to initialization issues 151 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 152. Bisecting K-means ▷ Bisecting K-means algorithm • Variant of K-means that can produce a partitioned or a hierarchical clustering 152 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 153. Bisecting K-means Example 153 K=4
- 154. Bisecting K-means Example 154 K=4 Produce a hierarchical clustering based on the sequence of clusterings produced
- 155. Limitations of K-means: Differing Sizes 155 ©Tan, Steinbach, Kumar Introduction to Data Mining Original Points K-means (3 Clusters) One solution is to use many clusters. Find parts of clusters, but need to put together K-means (10 Clusters)
- 156. Limitations of K-means: Differing Density 156 ©Tan, Steinbach, Kumar Introduction to Data Mining Original Points K-means (3 Clusters) One solution is to use many clusters. Find parts of clusters, but need to put together K-means (10 Clusters)
- 157. Limitations of K-means: Non-globular Shapes 157 ©Tan, Steinbach, Kumar Introduction to Data Mining Original Points K-means (2 Clusters) One solution is to use many clusters. Find parts of clusters, but need to put together K-means (10 Clusters)
- 158. Hierarchical Clustering ▷Produces a set of nested clusters organized as a hierarchical tree ▷Can be visualized as a dendrogram • A tree like diagram that records the sequences of merges or splits 158 ©Tan, Steinbach, Kumar Introduction to Data Mining 1 3 2 5 4 6 0 0.05 0.1 0.15 0.2 1 2 3 4 5 6 1 2 3 4 5
- 159. Strengths of Hierarchical Clustering ▷Do not have to assume any particular number of clusters • Any desired number of clusters can be obtained by ‘cutting’ the dendogram at the proper level ▷They may correspond to meaningful taxonomies • Example in biological sciences (e.g., animal kingdom, phylogeny reconstruction, …) 159 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 160. Density-Based Clustering ▷Clustering based on density (local cluster criterion), such as density-connected points ▷Each cluster has a considerable higher density of points than outside of the cluster 160
- 161. Density-Based Clustering Methods ▷Major features: • Discover clusters of arbitrary shape • Handle noise • One scan • Need density parameters as termination condition ▷Approaches • DBSCAN (KDD’96) • OPTICS (SIGMOD’99). • DENCLUE (KDD’98) • CLIQUE (SIGMOD’98) Data Mining 161
- 162. DBSCAN ▷Density = number of points within a specified radius (Eps) ▷A point is a core point if it has more than a specified number of points (MinPts) within Eps • These are points that are at the interior of a cluster ▷A border point has fewer than MinPts within Eps, but is in the neighborhood of a core point ▷A noise point is any point that is not a core point or a border point. 162 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 163. DBSCAN: Core, Border, and Noise Points 163 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 164. DBSCAN Examples 164 ©Tan, Steinbach, Kumar Introduction to Data Mining Original Points Point types: core, border and noise Eps = 10, MinPts = 4
- 165. When DBSCAN Works Well 165 ©Tan, Steinbach, Kumar Introduction to Data Mining Original Points Clusters • Resistant to Noise • Can handle clusters of different shapes and sizes
- 166. Recap: Data Mining Techniques 166 Predictive Tasks • Classification • Ranking • Regression • … Descriptive Tasks • Clustering • Association rules • Summarization • …
- 167. Evaluation Yi-Shin Chen Institute of Information Systems and Applications Department of Computer Science National Tsing Hua University yishin@gmail.com Many slides provided by Tan, Steinbach, Kumar for book “Introduction to Data Mining” are adapted in this presentation
- 168. Tasks in Data Mining ▷Problems should be well defined at the beginning ▷Two categories of tasks [Fayyad et al., 1996] 168 Predictive Tasks • Predict unknown values • e.g., potential customers Descriptive Tasks • Find patterns to describe data • e.g., Friendship finding VIP Cheap Potential
- 169. For Predictive Tasks 169
- 170. Metrics for Performance Evaluation 170 ©Tan, Steinbach, Kumar Introduction to Data Mining Focus on the predictive capability of a model Confusion Matrix: PREDICTED CLASS ACTUAL CLASS Class=Yes Class=No Class=Yes a b Class=No c d a: TP (true positive) b: FN (false negative) c: FP (false positive) d: TN (true negative)
- 171. Metrics for Performance Evaluation 171 ©Tan, Steinbach, Kumar Introduction to Data Mining Most widely-used metric: PREDICTED CLASS ACTUAL CLASS Class=Yes Class=No Class=Yes a (TP) b (FN) Class=No c (FP) d (TN) FNFPTNTP TNTP dcba da Accuracy
- 172. Limitation of Accuracy ▷ Consider a 2-class problem • Number of Class 0 examples = 9990 • Number of Class 1 examples = 10 ▷ If model predicts everything to be class 0, accuracy is 9990/10000 = 99.9 % ▷ Accuracy is misleading because model does not detect any class 1 example 172 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 173. Cost-Sensitive Measures 173 ©Tan, Steinbach, Kumar Introduction to Data Mining cba a pr rp ba a ca a 2 22 (F)measure-F (r)Recall (p)Precision Precision is biased towards C(Yes|Yes) & C(Yes|No) Recall is biased towards C(Yes|Yes) & C(No|Yes) F-measure is biased towards all except C(No|No) dwcwbwaw dwaw 4321 41 AccuracyWeighted
- 174. Test of Significance ▷ Given two models: • Model M1: accuracy = 85%, tested on 30 instances • Model M2: accuracy = 75%, tested on 5000 instances ▷ Can we say M1 is better than M2? • How much confidence can we place on accuracy of M1 and M2? • Can the difference in performance measure be explained as a result of random fluctuations in the test set? 174 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 175. Confidence Interval for Accuracy ▷ Prediction can be regarded as a Bernoulli trial • A Bernoulli trial has 2 possible outcomes → Possible outcomes for prediction: correct or wrong • Collection of Bernoulli trials has a Binomial distribution: → x ≈ Bin(N, p) x: number of correct predictions → e.g: Toss a fair coin 50 times, how many heads would turn up? Expected number of heads = N × p = 50 × 0.5 = 25 ▷ Given x (# of correct predictions) or equivalently, accuracy (ac)=x/N, and N (# of test instances) Can we predict p (true accuracy of model)? 175 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 176. Confidence Interval for Accuracy ▷For large test sets (N > 30), • ac has a normal distribution with mean p and variance p(1-p)/N • Confidence Interval for p: 176 ©Tan, Steinbach, Kumar Introduction to Data Mining Area = 1 - Z/2 Z1- /2 1 ) /)1( ( 2/12/ Z Npp pa ZP c )(2 442 2 2/ 22 2/2/ 2 2/ ZN aNaNZZZaN p ccc
- 177. Example :Comparing Performance of 2 Models ▷Given: M1: n1 = 30, e1 = 0.15 M2: n2 = 5000, e2 = 0.25 • d = |e2 – e1| = 0.1 (2-sided test) ▷At 95% confidence level, Z/2=1.96 177 ©Tan, Steinbach, Kumar Introduction to Data Mining 0043.0 5000 )25.01(25.0 30 )15.01(15.0 ˆ d 128.0100.00043.096.1100.0 t d Interval contains 0 : difference may not be statistically significant
- 178. For Descriptive Tasks 178
- 179. Computing Interestingness Measure ▷ Given a rule X Y, information needed to compute rule interestingness can be obtained from a contingency table 179 ©Tan, Steinbach, Kumar Introduction to Data Mining Y Y X f11 f10 f1+ X f01 f00 fo+ f+1 f+0 |T| Contingency table for X Y f11: support of X and Y f10: support of X and Y f01: support of X and Y f00: support of X and Y Used to define various measures support, confidence, lift, Gini, J-measure, etc.
- 180. Drawback of Confidence 180 ©Tan, Steinbach, Kumar Introduction to Data Mining Coffee Coffee Tea 15 5 20 Tea 75 5 80 90 10 100 Association Rule: Tea Coffee Confidence= P(Coffee|Tea) = 0.75 but P(Coffee) = 0.9 Although confidence is high, rule is misleading P(Coffee|Tea) = 0.9375
- 181. Statistical Independence ▷ Population of 1000 students • 600 students know how to swim (S) • 700 students know how to bike (B) • 420 students know how to swim and bike (S,B) • P(SB) = 420/1000 = 0.42 • P(S) P(B) = 0.6 0.7 = 0.42 • P(SB) = P(S) P(B) => Statistical independence • P(SB) > P(S) P(B) => Positively correlated • P(SB) < P(S) P(B) => Negatively correlated 181 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 182. Statistical-based Measures ▷Measures that take into account statistical dependence 182 ©Tan, Steinbach, Kumar Introduction to Data Mining )](1)[()](1)[( )()(),( )()(),( )()( ),( )( )|( YPYPXPXP YPXPYXP tcoefficien YPXPYXPPS YPXP YXP Interest YP XYP Lift
- 183. Example: Interest Factor 183 ©Tan, Steinbach, Kumar Introduction to Data Mining Coffee Coffee Tea 15 5 20 Tea 75 5 80 90 10 100 Association Rule: Tea Coffee Confidence= P(Coffee,Tea) = 0.15 P(Coffee) = 0.9, P(Tea) = 0.2 Interest = 0.15/(0.9×0.2)= 0.83 (< 1, therefore is negatively associated)
- 184. Subjective Interestingness Measure ▷Objective measure: • Rank patterns based on statistics computed from data • e.g., 21 measures of association (support, confidence, Laplace, Gini, mutual information, Jaccard, etc). ▷Subjective measure: • Rank patterns according to user’s interpretation → A pattern is subjectively interesting if it contradicts the expectation of a user (Silberschatz & Tuzhilin) → A pattern is subjectively interesting if it is actionable (Silberschatz & Tuzhilin) 184 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 185. Interestingness via Unexpectedness 185 ©Tan, Steinbach, Kumar Introduction to Data Mining Need to model expectation of users (domain knowledge) Need to combine expectation of users with evidence from data (i.e., extracted patterns) + Pattern expected to be frequent - Pattern expected to be infrequent Pattern found to be frequent Pattern found to be infrequent + - Expected Patterns- + Unexpected Patterns
- 186. Different Propose Measures 186 ©Tan, Steinbach, Kumar Introduction to Data Mining Some measures are good for certain applications, but not for others What criteria should we use to determine whether a measure is good or bad?
- 187. Comparing Different Measures 187 ©Tan, Steinbach, Kumar Introduction to Data Mining Example f11 f10 f01 f00 E1 8123 83 424 1370 E2 8330 2 622 1046 E3 9481 94 127 298 E4 3954 3080 5 2961 E5 2886 1363 1320 4431 E6 1500 2000 500 6000 E7 4000 2000 1000 3000 E8 4000 2000 2000 2000 E9 1720 7121 5 1154 E10 61 2483 4 7452 10 examples of contingency tables: Rankings of contingency tables using various measures:
- 188. Property under Variable Permutation Does M(A,B) = M(B,A)? Symmetric measures: support, lift, collective strength, cosine, Jaccard, etc Asymmetric measures: confidence, conviction, Laplace, J-measure, etc 188 ©Tan, Steinbach, Kumar Introduction to Data Mining B B A p q A r s A A B p r B q s
- 189. Cluster Validity ▷ For supervised classification we have a variety of measures to evaluate how good our model is • Accuracy, precision, recall ▷ For cluster analysis, the analogous question is how to evaluate the “goodness” of the resulting clusters? ▷ But “clusters are in the eye of the beholder”! ▷ Then why do we want to evaluate them? • To avoid finding patterns in noise • To compare clustering algorithms • To compare two sets of clusters • To compare two clusters 189 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 190. Clusters Found in Random Data 190 ©Tan, Steinbach, Kumar Introduction to Data Mining 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y Random Points 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y K- means 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y DBSCAN 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y Complete Link
- 191. Measures of Cluster Validity ▷ Numerical measures that are applied to judge various aspects of cluster validity, are classified into the following three types • External Index: Used to measure the extent to which cluster labels match externally supplied class labels, e.g., Entropy • Internal Index: Used to measure the goodness of a clustering structure without respect to external information, e.g., Sum of Squared Error (SSE) • Relative Index: Used to compare two different clusters ▷ Sometimes these are referred to as criteria instead of indices • However, sometimes criterion is the general strategy and index is the numerical measure that implements the criterion. 191 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 192. Measuring Cluster Validity Via Correlation ▷ Two matrices • Proximity Matrix • “Incidence” Matrix → One row and one column for each data point → An entry is 1 if the associated pair of points belong to the same cluster → An entry is 0 if the associated pair of points belongs to different clusters ▷ Compute the correlation between the two matrices • Since the matrices are symmetric, only the correlation between n(n-1) / 2 entries needs to be calculated. ▷ High correlation indicates that points that belong to the same cluster are close to each other. ▷ Not a good measure for some density or contiguity based clusters. 192 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 193. Measuring Cluster Validity Via Correlation ▷Correlation of incidence and proximity matrices for the K-means clustering of the following two data sets 193 ©Tan, Steinbach, Kumar Introduction to Data Mining 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y 0 0.2 0.4 0.6 0.8 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x y Corr = -0.9235 Corr = -0.5810
- 194. Internal Measures: SSE ▷ Clusters in more complicated figures aren’t well separated ▷ Internal Index: Used to measure the goodness of a clustering structure without respect to external information • Sum of Squared Error (SSE) ▷ SSE is good for comparing two clusters ▷ Can also be used to estimate the number of clusters 194 ©Tan, Steinbach, Kumar Introduction to Data Mining 2 5 10 15 20 25 30 0 1 2 3 4 5 6 7 8 9 10 K SSE 5 10 15 -6 -4 -2 0 2 4 6
- 195. Internal Measures: Cohesion and Separation ▷ Cluster Cohesion: Measures how closely related are objects in a cluster • Cohesion is measured by the within cluster sum of squares (SSE) ▷ Cluster Separation: Measure how distinct or well-separated a cluster is from other clusters • Separation is measured by the between cluster sum of squares • Where |Ci| is the size of cluster i 195 ©Tan, Steinbach, Kumar Introduction to Data Mining i Cx i i mxWSS 2 )( i ii mmCBSS 2 )(
- 196. Final Comment on Cluster Validity “The validation of clustering structures is the most difficult and frustrating part of cluster analysis. Without a strong effort in this direction, cluster analysis will remain a black art accessible only to those true believers who have experience and great courage.” Algorithms for Clustering Data, Jain and Dubes 196 ©Tan, Steinbach, Kumar Introduction to Data Mining
- 197. Case Studies Yi-Shin Chen Institute of Information Systems and Applications Department of Computer Science National Tsing Hua University yishin@gmail.com Many slides provided by Tan, Steinbach, Kumar for book “Introduction to Data Mining” are adapted in this presentation
- 198. Case: Mining Reddit Data Please check the data set during the breaks 198
- 199. Reddit Data https://drive.google.com/open?id=0BwpI8947eCyuRFVDLU4tT2 5JbFE 199
- 200. Reddit: The Front Page of the Internet 50k+ on this set
- 201. Subreddit Categories ▷Reddit’s structure may already provide a baseline similarity
- 202. Provided Data
- 203. Recover Structure

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