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• Z-score normalization (μ: mean, σ: standard deviation):
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Data preprocessing in Data Mining
- 1. Data Preprocessing Jun Du The University of Western Ontario jdu43@uwo.ca
- 2. Outline • Data • Data Preprocessing: An Overview • Data Cleaning • Data Transformation and Data Discretization • Data Reduction • Summary 1
- 3. What is Data? • Collection of data objects and their attributes • Data objects rows • Attributes columns 2 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 Attributes Objects
- 4. Data Objects • A data object represents an entity. • Examples: – Sales database: customers, store items, sales – Medical database: patients, treatments – University database: students, professors, courses • Also called examples, instances, records, cases, samples, data points, objects, etc. • Data objects are described by attributes. 3
- 5. Attributes • An attribute is a data field, representing a characteristic or feature of a data object. • Example: – Customer Data: customer _ID, name, gender, age, address, phone number, etc. – Product data: product_ID, price, quantity, manufacturer, etc. • Also called features, variables, fields, dimensions, etc. 4
- 6. Attribute Types (1) • Nominal (Discrete) Attribute – Has only a finite set of values (such as, categories, states, etc.) – E.g., Hair_color = {black, blond, brown, grey, red, white, …} – E.g., marital status, zip codes • Numeric (Continuous) Attribute – Has real numbers as attribute values – E.g., temperature, height, or weight. • Question: what about student id, SIN, year of birth? 5
- 7. Attribute Types (2) • Binary – A special case of nominal attribute: with only 2 states (0 and 1) – Gender = {male, female}; – Medical test = {positive, negative} • Ordinal – Usually a special case of nominal attribute: values have a meaningful order (ranking) – Size = {small, medium, large} – Army rankings 6
- 8. Outline • Data • Data Preprocessing: An Overview • Data Cleaning • Data Transformation and Data Discretization • Data Reduction • Summary 7
- 9. Data Preprocessing • Why preprocess the data? – Data quality is poor in real world. – No quality data, no quality mining results! • Measures for data quality – Accuracy: noise, outliers, … – Completeness: missing values, … – Redundancy: duplicated data, irrelevant data, … – Consistency: some modified but some not, … – …… 8
- 10. Typical Tasks in Data Preprocessing • Data Cleaning – Handle missing values, noisy / outlier data, resolve inconsistencies, … • Data Transformation – Aggregation – Type Conversion – Normalization • Data Reduction – Data Sampling – Dimensionality Reduction • …… 9
- 11. Outline • Data • Data Preprocessing: An Overview • Data Cleaning • Data Transformation and Data Discretization • Data Reduction • Summary 10
- 12. Data Cleaning • Missing value: lacking attribute values – E.g., Occupation = “ ” • Noise (Error): modification of original values – E.g., Salary = “−10” • Outlier: considerably different from most of the other data (not necessarily error) – E.g., Salary = “2,100,000” • Inconsistency: discrepancies in codes or names – E.g., Age=“42”, Birthday=“03/07/2010” – Was rating “1, 2, 3”, now rating “A, B, C” • …… 11
- 13. 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 – Human / Hardware / Software problems • E.g., Birthdate information is accidentally deleted for all people born in 1988. – …… 12
- 14. How to Handle Missing Value? • Eliminate ignore missing value • Eliminate ignore the examples • Eliminate ignore the features • Simple; not applicable when data is scarce • Estimate missing value – Global constant : e.g., “unknown”, – Attribute mean (median, mode) – Predict the value based on features (data imputation) • Estimate gender based on first name (name gender) • Estimate age based on first name (name popularity) • Build a predictive model based on other features – Missing value estimation depends on the missing reason! 13
- 15. Demonstration • ReplaceMissingValues – WekaVote – Replacing missing values for nominal and numeric attributes • More functions in Rapidminer 14
- 16. Noisy (Outlier) Data • Noise: refers to modification of original values • Incorrect attribute values may be due to – faulty data collection instruments – data entry problems – data transmission problems – technology limitation – inconsistency in naming convention 15
- 17. How to Handle Noisy (Outlier) Data? • Binning – first sort data and partition into (equal-frequency) bins – then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. • Regression – smooth by fitting the data into regression functions • Clustering – detect and remove outliers • Combined computer and human inspection – detect suspicious values and check by human 16
- 18. Binning Sort data in ascending order: 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 • Partition into equal-frequency (equal-depth) bins: – Bin 1: 4, 8, 9, 15 – Bin 2: 21, 21, 24, 25 – Bin 3: 26, 28, 29, 34 • Smoothing by bin means: – Bin 1: 9, 9, 9, 9 – Bin 2: 23, 23, 23, 23 – Bin 3: 29, 29, 29, 29 • Smoothing by bin boundaries: – Bin 1: 4, 4, 4, 15 – Bin 2: 21, 21, 25, 25 – Bin 3: 26, 26, 26, 34 17
- 19. Regression 18 x y y = x + 1 X1 Y1 Y1’
- 21. Outline • Data • Data Preprocessing: An Overview • Data Cleaning • Data Transformation and Data Discretization • Data Reduction • Summary 20
- 22. Data Transformation • Aggregation: – Attribute / example summarization • Feature type conversion: – Nominal Numeric, … • Normalization: – Scaled to fall within a small, specified range • Attribute/feature construction: – New attributes constructed from the given ones 21
- 23. Aggregation • Combining two or more attributes (examples) into a single attribute (example) • Combining two or more attribute values into a single attribute value • Purpose – Change of scale • Cities aggregated into regions, states, countries, etc – More “stable” data • Aggregated data tends to have less variability – More “predictive” data • Aggregated data might have high Predictability 22
- 24. Demonstration • MergeTwoValues – Wekacontact-lenses – Merge class values “soft” and “hard” • Effective aggregation in real-world application 23
- 25. Feature Type Conversion • Some algorithms can only handle numeric features; some can only handle nominal features. Only few can handle both. • Features have to be converted to satisfy the requirement of learning algorithms. – Numeric Nominal (Discretization) • E.g., Age Discretization: Young 18-29; Career 30-40; Mid-Life 41-55; Empty-Nester 56-69; Senior 70+ – Nominal Numeric • Introduce multiple numeric features for one nominal feature • Nominal Binary (Numeric) • E.g., size={L, M, S} size_L: 0, 1; size_M: 0, 1; size_S: 0, 1 24
- 26. Demonstration • Discretize – Wekadiabetes – Discretize “age” (equal bins vs equal frequency) • NumericToNominal – Wekadiabetes – Discretize “age” (vs “Discretize” method) • NominalToBinary – UCIautos – Convert “num-of-doors” – Convert “drive-wheels” 25
- 27. Normalization 716.00)00.1( 000,12000,98 000,12600,73 26 Scale the attribute values to a small specified range • Min-max normalization: to [new_minA, new_maxA] – E.g., Let income range $12,000 to $98,000 normalized to [0.0, 1.0]. Then $73,000 is mapped to • Z-score normalization (μ: mean, σ: standard deviation): • …… AAA AA A minnewminnewmaxnew minmax minv v _)__('
- 28. Demonstration • Normalize – Wekadiabetes – Normalize “age” • Standardize – Wekadiabetes – Standardize “age” (vs “Normalize” method) 27
- 29. Outline • Data • Data Preprocessing: An Overview • Data Cleaning • Data Transformation and Data Discretization • Data Reduction • Summary 28
- 30. Sampling • Big data era: too expensive (or even infeasible) to process the entire data set • Sampling: obtaining a small sample to represent the entire data set ( ---- undersampling) • Oversampling is also required in some scenarios, such as class imbalance problem – E.g., 100 HIV test results: 5 positive, 995 negative 29
- 31. Sampling Principle Key principle for effective sampling: • Using a sample will work almost as well as using the entire data sets, if the sample is representative • A sample is representative if it has approximately the same property (of interest) as the original set of data 30
- 32. Types of Sampling (1) • Random sampling without replacement – As each example is selected, it is removed from the population • Random sampling with replacement – Examples are not removed from the population after being selected • The same example can be picked up more than once 31 Raw Data
- 33. Types of Sampling (2) • Stratified sampling – Split the data into several partitions; then draw random samples from each partition 32 Raw Data Stratified Sampling
- 34. Demonstration • Resample – UCIwaveform-5000 – Undersampling (with or without replacement) 33
- 35. Dimensionality Reduction • Purpose: – 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 – Feature Selection – Feature Extraction 34
- 36. Feature Selection • Redundant features – Duplicated information contained in different features – E.g., “Age”, “Year of Birth”; “Purchase price”, “Sales tax” • Irrelevant features – Containing no information that is useful for the task – E.g., students' ID is irrelevant to predicting GPA • Goal: – A minimum set of features containing all (most) information 35
- 37. Heuristic Search in Feature Selection • Given d features, there are 2d possible feature combinations – Exhaust search won’t work – Heuristics has to be applied • Typical heuristic feature selection methods: – Feature ranking – Forward feature selection – Backward feature elimination – Bidirectional search (selection + elimination) – Search based on evolution algorithm – …… 36
- 38. Feature Ranking • Steps: 1) Rank all the individual features according to certain criteria (e.g., information gain, gain ratio, χ2) 2) Select / keep top N features • Properties: – Usually independent of the learning algorithm to be used – Efficient (no search process) – Hard to determine the threshold – Unable to consider correlation between features 37
- 39. Forward Feature Selection • Steps: 1) First select the best single-feature (according to the learning algorithm) 2) Repeat (until some stop criterion is met): Select the next best feature, given the already picked features • Properties: – Usually learning algorithm dependent – Feature correlation is considered – More reliable – Inefficient 38
- 40. Backward Feature Elimination • Steps: 1) First build a model based on all the features 2) Repeat (until some criterion is met): Eliminate the feature that makes the least contribution. • Properties: – Usually learning algorithm dependent – Feature correlation is considered – More reliable – Inefficient 39
- 41. Filter vs Wrapper Model • Filter model – Separating feature selection from learning – Relying on general characteristics of data (information, etc.) – No bias toward any learning algorithm, fast – Feature ranking usually falls into here • Wrapper model – Relying on a predetermined learning algorithm – Using predictive accuracy as goodness measure – High accuracy, computationally expensive – FFS, BFE usually fall into here 40
- 42. Demonstration • Feature ranking – Wekaweather – ChiSquared, InfoGain, GainRatio • FFS & BFE – WekaDiabetes – ClassifierSubsetEval + GreedyStepwise 41
- 43. Feature Extraction • Map original high-dimensional data onto a lower- dimensional space – Generate a (smaller) set of new features – Preserve all (most) information from the original data • Techniques – Principal Component Analysis (PCA) – Canonical Correlation Analysis (CCA) – Linear Discriminant Analysis (LDA) – Independent Component Analysis (ICA) – Manifold Learning – …… 42
- 44. Principal Component Analysis (PCA) • Find a projection that captures the largest amount of variation in data • The original data are projected onto a much smaller space, resulting in dimensionality reduction. 43 x2 x1 e
- 45. Principal Component Analysis (Steps) • Given data from n-dimensions (n features), find k ≤ n new features (principal components) that can best represent data – Normalize input data: each feature falls within the same range – Compute k principal components (details omitted) – Each input data is projected in the new k-dimensional space – The new features (principal components ) are sorted in order of decreasing “significance” or strength – Eliminate weak components / features to reduce dimensionality. • Works for numeric data only 44
- 46. PCA Demonstration • UCIbreast-w – Accuracy with all features – PrincipalComponents (data transformation) – Visualize/save transformed data (first two features, last two features) – Accuracy with all transformed features – Accuracy with top 1 or 2 feature(s) 45
- 47. Outline • Data • Data Preprocessing: An Overview • Data Cleaning • Data Transformation and Data Discretization • Data Reduction • Summary 46
- 48. Summary • Data (features and instances) • Data Cleaning: missing values, noise / outliers • Data Transformation: aggregation, type conversion, normalization • Data Reduction – Sampling: random sampling with replacement, random sampling without replacement, stratified sampling – Dimensionality reduction: • Feature Selection: Feature ranking, FFS, BFE • Feature Extraction: PCA 47
- 49. Notes • In real world applications, data preprocessing usually occupies about 70% workload in a data mining task. • Domain knowledge is usually required to do good data preprocessing. • To improve a predictive performance of a model – Improve learning algorithms (different algorithms, different parameters) • Most data mining research focuses on here – Improve data quality ---- data preprocessing • Deserve more attention! 48