DATA MINING DEPARTMENT OF COMPUTER SCIENCE , UNIVERSITY OF

DATA MINING DEPARTMENT OF COMPUTER SCIENCE , UNIVERSITY OF COLORADO, COLORADO SPRINGS. CS 4334/5334 AND DASE 4435 DATA MINING, DR. OLUWATOSIN OLUWADARE, 2022 FALL 2022 Lecture 4-6: Data Preprocessing Slides Adapted from Jiawei Han et al. and Jianlin Cheng

Data Mining: Concepts and Techniques (3rd ed.) — Chapter 3 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2011 Han, Kamber & Pei. All rights reserved. 2

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary 3

Why Data Preprocessing? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data noisy: containing errors or outliers e.g., occupation “ ” e.g., Salary “-10” inconsistent: containing discrepancies in codes or names e.g., Age “42” Birthday “03/07/1997” e.g., Was rating “1,2,3”, now rating “A, B, C” e.g., discrepancy between duplicate records 4/16/23 4

Why Is Data Dirty? Incomplete data may come from Noisy data (incorrect values) may come from Faulty data collection instruments Human or computer error at data entry Errors in data transmission Inconsistent data may come from “Not applicable” data value when collected Different considerations between the time when the data was collected and when it is analyzed. Human/hardware/software problems Different data sources Functional dependency violation (e.g., modify some linked data) Duplicate records also need data cleaning 4/16/23 5

Why Is Data Preprocessing Important? No quality data, no quality mining results! Quality decisions must be based on quality data e.g., duplicate or missing data may cause incorrect or even misleading statistics. Data warehouse needs consistent integration of quality data Data extraction, cleaning, and transformation comprises the majority of the work of building a data warehouse. Low quality data lead to low quality mining result. 4/16/23 6

Data Quality: Why Preprocess the Data? Data have quality if they satisfy the requirements of the intended use. Measures for data quality: A multidimensional view Accuracy: correct or wrong, accurate or not Completeness: not recorded, unavailable, Consistency: some modified but some not, dangling, Timeliness: timely update? Believability: how trustable the data are correct? Interpretability: how easily the data can be

Major Tasks in Data Preprocessing Data cleaning Data integration Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Integration of multiple databases, data cubes, or files Data reduction Dimensionality reduction Numerosity reduction Data compression Data transformation and data discretization Normalization Concept hierarchy generation

Forms of Data Preprocessing 4/16/23 Data Mining: Concepts and Techniques 9

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary 10

Data Cleaning Data in the Real World Is Dirty: Lots of potentially incorrect data, e.g., instrument faulty, human or computer error, transmission error incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data noisy: containing noise, errors, or outliers e.g., Occupation “ ” (missing data) e.g., Salary “ 10” (an error) inconsistent: containing discrepancies in codes or names, e.g., Age “42”, Birthday “03/07/2010” Was rating “1, 2, 3”, now rating “A, B, C” discrepancy between duplicate records Intentional (e.g., disguised missing data)

Incomplete (Missing) Data Data is not always available E.g., many tuples have no recorded value for several attributes, such as customer income in sales data Missing data may be due to equipment malfunction inconsistent with other recorded data and thus deleted data not entered due to misunderstanding certain data may not be considered important at the time of entry not register history or changes of the data

Customer Data Name Age Sex Income Class Mike 40 Male 150k Big spender Jenny 20 Female ? Regular Data Mining: Concepts and Techniques 4/16/23 13

Incomplete (Missing) Data Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite

How to Handle Missing Data? Ignore the tuple: usually done when class label is missing (when doing classification)—not effective when the % of missing values per attribute varies considerably Fill in the missing value manually: tedious infeasible? Fill in it automatically with a global constant : e.g., “unknown”, a new class?! the attribute mean the attribute mean for all samples belonging to the same class: smarter

Noisy Data Noise: random error or variance in a measured variable Incorrect attribute values may be due to faulty data collection instruments data entry problems data transmission problems technology limitation inconsistency in naming convention Other data problems which require data cleaning duplicate records incomplete data inconsistent data 16

How to Handle Noisy Data? Binning first sort data and partition into (equalfrequency) 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 (e.g., deal with possible outliers)

Simple Discretization Methods: Binning Equal-width (distance) partitioning Divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W (B –A)/N. The most straightforward, but outliers may dominate presentation Skewed data is not handled well Equal-depth (frequency) partitioning Divides the range into N intervals, each containing approximately same number of samples Data Mining: Concepts and Good data scaling Techniques 4/16/23 18

Binning Methods for Data Smoothing Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into equal-frequency (equi-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 Data Mining: Concepts and Techniques 4/16/23 19

How to Handle Noisy Data?

Data Cleaning as a Process Data discrepancy detection Use metadata (e.g., domain, range, dependency, distribution) Check field overloading Check uniqueness rule, consecutive rule and null rule Use commercial tools Data scrubbing: use simple domain knowledge (e.g., postal code, spell-check) to detect errors and make corrections Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers) Data migration and integration Data migration tools: allow transformations to be specified ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface Integration of the two processes

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary 22

Data Integration Data integration: Schema integration: e.g., A.cust-id B.cust-# Combines data from multiple sources into a coherent store Integrate metadata from different sources Entity identification problem: Identify real world entities from multiple data sources, e.g., Bill Clinton William Clinton Detecting and resolving data value conflicts For the same real world entity, attribute values from different sources are different Possible reasons: different representations, different scales, e.g., metric vs. British units 23

Handling Redundancy in Data Integration Redundant data occur often when integration of multiple databases Object identification: The same attribute or object may have different names in different databases Derivable data: One attribute may be a “derived” attribute in another table, e.g., annual revenue Redundant attributes may be able to be detected by correlation analysis and covariance analysis Careful integration of the data from multiple sources may help reduce/avoid redundancies and 24

Correlation Analysis (Nominal Data) Χ2 (chi-square) test The larger the Χ2 value, the more likely the variables are related The cells that contribute the most to the Χ2 value are those whose actual count is very different from the expected count Correlation does not imply causality # of hospitals and # of car-theft in a city are correlated Both are causally linked to the third variable: population

Chi-Square Calculation: An Example Play chess Not play chess Sum (row) Like science fiction 250 200 450 Not like science fiction 50 1000 1050 Sum(col.) 300 1200 1500

Chi-Square Calculation: An Example Steps to follow State the hypothesis Establish the significance level you need (p 0.001, 99.9% confidence level) and the number of degrees of freedom Calculate the expected values Use the observed and expected values to calculate the chi-square test statistic Compare the chi-square statistic with the critical value from the table Make a decision about your hypothesis

Hypothesis Play chess Not play chess Sum (row) Like science fiction 250 200 450 Not like science fiction 50 1000 1050 Sum(col.) 300 1200 1500 H0 : Chess Playing or not and preferred reading are independent(no association). H1 : Chess Playing or not and preferred reading are not independent(association).

Degrees of freedom Significance level, p 0.001 Number of degrees of freedom is calculated by multiplying the number of rows minus 1 by the number of columns minus 1 Degree of freedom (r -1) x (c-1) For a 2 x 2 contingency table: (2-1) x (2-1) 1

Expected Values Observed Values Play chess Not play chess Sum (row) Like science fiction 250 200 450 Not like science fiction 50 1000 1050 Sum(col.) 300 1200 1500 Play chess Not play chess Sum (row) Like science fiction 90 360 450 Not like science fiction 210 840 1050 Sum(col.) 300 1200 1500 Expected Values

Calculation 284.44 121.90 71.11 30.48 507.93

Table of Critical values ?

Make a Decision: Is it Significant? Test value table value then REJECT H0 507.93 10.828 We reject H0 ’ Chess Playing or not and preferred reading are independent’ Instead, we conclude that the two attributes are (strongly) correlated for the given group of people.

Chi-Square Calculation: An Example Play chess Not play chess Sum (row) Like science fiction 250(90) 200(360) 450 Not like science fiction 50(210) 1000(840) 1050 Sum(col.) 300 1200 1500 Χ2 (chi-square) calculation (numbers in parenthesis are expected counts calculated based on the data distribution in the two categories) It shows that like science fiction and play chess are correlated in the group

Chi Square Distribution Degree of freedom 1 (e.g., (r 1)(c 1)) For 0.001 significance, threshold 10.828 http://en.wikipedia.org/wiki/Pearson's chi-squared test Data Mining: Concepts and 4/16/23 Techniques 35

Correlation Analysis (Numeric Data) Correlation coefficient (also called Pearson’s product moment coefficient) where n is the number of tuples, and are the respective means of A and B, σA and σB are the respective standard deviation of A and B, and Σ(aibi) is the sum of the AB cross-product. If rA,B 0, A and B are positively correlated (A’s values increase as B’s). The higher, the stronger correlation.

Visually Evaluating Correlation Scatter plots showing the similarity from –1 to 1.

Correlation (viewed as linear relationship) Correlation measures the linear relationship between objects To compute correlation, we standardize data objects, A and B, and then take their dot product

Covariance (Numeric Data) Covariance is similar to correlation Correlation coefficient: where n is the number of tuples, and are the respective mean or expected values of A and B, σA and σB are the respective standard deviation of A and B. Positive covariance: If CovA,B 0, then A and B both tend to be larger than their expected values. Negative covariance: If CovA,B 0 then if A is larger than its expected value, B is likely to be smaller than its expected value. Independence: CovA,B 0 but the converse is not true: Some pairs of random variables may have a covariance of 0 but are not independent. Only under some additional assumptions (e.g., the data follow multivariate normal distributions) does a covariance of 0

Co-Variance: An Example It can be simplified in computation as Suppose two stocks A and B have the following values in one week: (2, 5), (3, 8), (5, 10), (4, 11), (6, 14). Question: If the stocks are affected by the same industry trends, will their prices rise or fall together? E(A) (2 3 5 4 6)/ 5 20/5 4 E(B) (5 8 10 11 14) /5 48/5 9.6 Cov(A,B) (2 5 3 8 5 10 4 11 6 14)/5 4 9.6 4 Thus, A and B rise together since Cov(A, B) 0.

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary (Lecture 5) 41

Data Reduction Strategies Data reduction: Obtain a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results Why data reduction? — A database/data warehouse may store terabytes of data. Complex data analysis may take a very long time to run on the complete data set. Data reduction strategies Dimensionality reduction, e.g., remove unimportant attributes Wavelet transforms Principal Components Analysis (PCA) Feature subset selection, feature creation Numerosity reduction (some simply call it: Data Reduction) Regression and Log-Linear Models Histograms, clustering, sampling Data cube aggregation Data compression

Data Reduction 1: Dimensionality Reduction Curse of dimensionality When dimensionality increases, data becomes increasingly sparse Density and distance between points, which is critical to clustering, outlier analysis, becomes less meaningful The possible combinations of subspaces will grow exponentially Dimensionality reduction Avoid the curse of dimensionality Help eliminate irrelevant features and reduce noise Reduce time and space required in data mining Allow easier visualization Dimensionality reduction techniques Wavelet transforms Principal Component Analysis Supervised and nonlinear techniques (e.g., feature selection)

Mapping Data to a New Space Fourier transform Wavelet transform Two Sine Waves Two Sine Waves Noise Frequency

What Is Wavelet Transform? Decomposes a signal into different frequency subbands Applicable to ndimensional signals Data are transformed to preserve relative distance between objects at different levels of resolution Allow natural clusters to become more distinguishable

(1) Wavelet Transformation Haar2 Daubechie4 Discrete wavelet transform (DWT) for linear signal processing, multi-resolution analysis Compressed approximation: store only a small fraction of the strongest of the wavelet coefficients Similar to discrete Fourier transform (DFT), but better lossy compression, localized in space Method: Length, L, must be an integer power of 2 (padding with 0’s, when necessary) Each transform has 2 functions: smoothing, difference Applies to pairs of data, resulting in two set of data of length L/2 Applies two functions recursively, until reaches the desired

Wavelet Decomposition Wavelets: A math tool for space-efficient hierarchical decomposition of functions S [2, 2, 0, 2, 3, 5, 4, 4] can be transformed to S [23/4, -11/4, 1/2, 0, 0, -1, -1, 0] Compression: many small detail coefficients can be replaced by 0’s, and only the significant coefficients are retained

Haar Wavelet Coefficients Coefficient “Supports” 2.75 Hierarchical 2.75 decomposition structure (a.k.a. “error tree”) -1.25 0.5 0 2 -1.25 - -1 -1 - 2 0 2 3 0 0 0 - 5 4 Original frequency distribution 4 0 -1 -1 0 - 0.5 - - - - - -

Why Wavelet Transform? Use hat-shape filters Emphasize region where points cluster Suppress weaker information in their boundaries Effective removal of outliers Insensitive to noise, insensitive to input order Multi-resolution Detect arbitrary shaped clusters at different scales Efficient Complexity O(N) Only applicable to low dimensional data

Why Wavelet Transform? Wavelet transforms give good results on sparse or skewed data and on data with ordered attributes. Wavelet transforms have many real-world applications, including the compression of fingerprint images, computer vision, analysis of time-series data, and data cleaning

(2) 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. We find the eigenvectors of the covariance matrix, and these eigenvectors define the new space x2 e x1

Principal Component Analysis (PCA) 4/16/23 Data Mining: Concepts and Techniques 52

PCA Method 4/16/23 Data Mining: Concepts and Techniques 53

Principal Component Analysis (Steps) Given N data vectors from n-dimensions, find k n orthogonal vectors (principal components) that can be best used to represent data Normalize input data: Each attribute falls within the same range Compute k orthonormal (unit) vectors, i.e., principal components Each input data (vector) is a linear combination of the k principal component vectors The principal components are sorted in order of decreasing “significance” or strength Since the components are sorted, the size of the data can be reduced by eliminating the weak components, i.e., those with low variance (i.e., using the strongest principal

Principal Component Analysis X2 Y1 Y2 X1 4/16/23 Data Mining: Concepts and Techniques 55

(3) Attribute Subset Selection Another way to reduce dimensionality of data Redundant attributes Duplicate much or all of the information contained in one or more other attributes E.g., purchase price of a product and the amount of sales tax paid Irrelevant attributes 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

Heuristic Search in Attribute Selection There are 2d possible attribute combinations of d attributes Typical heuristic attribute selection methods: Best single attribute under the attribute independence assumption: choose by significance tests Best step-wise feature selection: The best single-attribute is picked first Then next best attribute condition to the first, . Step-wise attribute elimination: Repeatedly eliminate the worst attribute Best combined attribute selection and elimination

(4) Attribute Creation (Feature Generation) Create new attributes (features) that can capture the important information in a data set more effectively than the original ones Three general methodologies Attribute extraction Domain-specific Mapping data to new space (see: data reduction) E.g., Fourier transformation, wavelet transformation, manifold approaches (not covered) Attribute construction Combining features (see: discriminative frequent patterns in Chapter 7) 58

Data Reduction 2: Numerosity Reduction Reduce data volume by choosing alternative, smaller forms of data representation Parametric methods (e.g., regression) Assume the data fits some model, estimate model parameters, store only the parameters, and discard the data (except possible outliers) Ex.: Log-linear models—obtain value at a point in m-D space as the product on appropriate marginal subspaces Non-parametric methods Do not assume models Major families: histograms, clustering, sampling,

Parametric Data Reduction: Regression and Log-Linear Models Linear regression Data modeled to fit a straight line Often uses the least-square method to fit the line Multiple regression Allows a response variable Y to be modeled as a linear function of multidimensional feature vector Log-linear model Approximates discrete multidimensional probability distributions

(1) Regression Analysis y Y1 Regression analysis: A collective Y1’ name for techniques for the modeling y x 1 and analysis of numerical data consisting of values of a dependent variable (also called response X1 x variable or measurement) and of one or more independent variables (aka. explanatory variables or predictors) The parameters are estimated so as to give a "best fit" of the data Most commonly the best fit is evaluated by using the least Used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships

Regress Analysis and LogLinear Models Linear regression: Y w X b Two regression coefficients, w and b, specify the line and are to be estimated by using the data at hand Using the least squares criterion to the known values of Y1, Y2, , X1, X2, . Multiple regression: Y b0 b1 X1 b2 X2 Many nonlinear functions can be transformed into the above Log-linear models: Approximate discrete multidimensional probability distributions Estimate the probability of each point (tuple) in a multidimensional space for a set of discretized attributes, based

(2) Histogram Analysis Divide data into buckets and store average (sum) for each bucket Partitioning rules: Equal-width: equal bucket range Equal-frequency (or equal-depth)

(3) Clustering Partition data set into clusters based on similarity, and store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multi-dimensional index tree structures There are many choices of clustering definitions and clustering algorithms Cluster analysis will be studied in depth in Chapter 10

Cluster Analysis 4/16/23 Data Mining: Concepts and Techniques 65

(4) Sampling Sampling: obtaining a small sample s to represent the whole data set N Allow a mining algorithm to run in complexity that is potentially sub-linear to the size of the data Key principle: Choose a representative subset of the data Simple random sampling may have very poor performance in the presence of skew Develop adaptive sampling methods, e.g., stratified sampling: Note: Sampling may not reduce database I/Os

Types of Sampling Simple random sampling There is an equal probability of selecting any particular item Sampling without replacement Once an object is selected, it is removed from the population Sampling with replacement A selected object is not removed from the population Stratified sampling: Partition the data set, and draw samples from each partition (proportionally, i.e., approximately the same percentage of the data) Used in conjunction with skewed data

Sampling: With or without Replacement R O W SRS le random t p u o m i h t s i ( w e l samp ment) e c a l p re SRSW R Raw Data

Sampling: Cluster or Stratified Sampling Raw Data Cluster/Stratified Sample

Data Cube Aggregation The lowest level of a data cube (base cuboid) Further reduce the size of data to deal with Reference appropriate levels E.g., a customer in a phone calling data warehouse Multiple levels of aggregation in data cubes The aggregated data for an individual entity of interest Use the smallest representation which is enough to solve the task Queries regarding aggregated information should

Data Reduction 3: Data Compression String compression There are extensive theories and well-tuned algorithms Typically lossless, but only limited manipulation is possible without expansion Audio/video compression Typically lossy compression, with progressive refinement Sometimes small fragments of signal can be reconstructed without reconstructing the whole Time sequence is not audio Typically short and vary slowly with time Dimensionality and numerosity reduction may also

Data Compression Compressed Data Original Data lossless Original Data Approximated sy s o l

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary

Data Transformation A function that maps the entire set of values of a given attribute to a new set of replacement values s.t. each old value can be identified with one of the new values Methods Smoothing: Remove noise from data Attribute/feature construction New attributes constructed from the given ones Aggregation: Summarization, data cube construction Normalization: Scaled to fall within a smaller, specified range min-max normalization z-score normalization normalization by decimal scaling

Data Transformation: Normalization Min-max normalization: to [new minA, new maxA] Z-score normalization (μ: mean, σ: standard deviation): Ex. Let income range 12,000 to 98,000 normalized to [0.0, 1.0]. Then 73,000 is mapped to Ex. Let μ 54,000, σ 16,000. Then Normalization by decimal scaling Where j is the smallest integer such that Max( ν’ ) 1

Normalization

Normalization : Z-Score

Normalization Min-Max Z-Score

Discretization Three types of attributes Nominal—values from an unordered set, e.g., color, profession Ordinal—values from an ordered set, e.g., military or academic rank Numeric—real numbers, e.g., integer or real numbers Discretization: Divide the range of a continuous attribute into intervals Interval labels can then be used to replace actual data values Reduce data size by discretization Supervised vs. unsupervised Split (top-down) vs. merge (bottom-up) Discretization can be performed recursively on an attribute

Discretization For example, we can divide a continuous variable, weight, and store it in the following groups: Under 100 lbs (light), between 140–160 lbs (mid), and over 200 lbs (heavy) Therefore, discretization helps make our data easier to understand if it fits the problem statement.

Data Discretization Methods Typical methods: All the methods can be applied recursively Binning Histogram analysis Top-down split, unsupervised Top-down split, unsupervised Clustering analysis (unsupervised, top-down split or bottom-up merge) Decision-tree analysis (supervised, top-down split) 2 Correlation (e.g., ) analysis (unsupervised, 81

Simple Discretization: Binning Equal-width (distance) partitioning Divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W (B –A)/N. The most straightforward, but outliers may dominate presentation Skewed data is not handled well Equal-depth (frequency) partitioning Divides the range into N intervals, each containing approximately same number of samples Good data scaling

Binning Methods for Data Smoothing Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into equal-frequency (equi-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

Class Labels (Binning vs. Clustering) Data Equal frequency (binning) Equal interval width (binning) K-means clustering leads to better results 84

Classification & Correlation Analysis Classification (e.g., decision tree analysis) Supervised: Given class labels, e.g., cancerous vs. benign Using entropy to determine split point (discretization point) Top-down, recursive split Details to be covered in Chapter 7 Correlation analysis (e.g., Chi-merge: χ2-based discretization) Supervised: use class information Bottom-up merge: find the best neighboring intervals (those having similar distributions of classes, i.e., low χ2 values) to merge 85

Concept Hierarchy Generation Concept hierarchy organizes concepts (i.e., attribute values) hierarchically and is usually associated with each dimension in a data warehouse Concept hierarchies facilitate drilling and rolling in data warehouses to view data in multiple granularity Concept hierarchy formation: Recursively reduce the data by collecting and replacing low level concepts (such as numeric values for age) by higher level concepts (such as youth, adult, or senior) Concept hierarchies can be explicitly specified by domain experts and/or data warehouse designers Concept hierarchy can be automatically formed for both numeric and nominal data. For numeric data, use discretization methods shown.

Concept Hierarchy Generation for Nominal Data Specification of a partial/total ordering of attributes explicitly at the schema level by users or experts Specification of a hierarchy for a set of values by explicit data grouping {Urbana, Champaign, Chicago} Illinois Specification of only a partial set of attributes street city state country E.g., only street city, not others Automatic generation of hierarchies (or attribute levels) by the analysis of the number of distinct values E.g., for a set of attributes: {street, city, state,

Automatic Concept Hierarchy Generation Some hierarchies can be automatically generated based on the analysis of the number of distinct values per attribute in the data set The attribute with the most distinct values is placed at the lowest level of the hierarchy Exceptions, e.g., weekday, month, quarter, year country 15 distinct values province or state 365 distinct values city 3567 distinct values street 674,339 distinct values

Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major Tasks in Data Preprocessing Data Cleaning Data Integration Data Reduction Data Transformation and Data Discretization Summary

Summary Data quality: accuracy, completeness, consistency, timeliness, believability, interpretability Data cleaning: e.g. missing/noisy values, outliers Data integration from multiple sources: Entity identification problem Remove redundancies Detect inconsistencies Data reduction Dimensionality reduction Numerosity reduction Data compression Data transformation and data discretization Normalization Concept hierarchy generation

References D. P. Ballou and G. K. Tayi. Enhancing data quality in data warehouse environments. Comm. of ACM, 42:73-78, 1999 A. Bruce, D. Donoho, and H.-Y. Gao. Wavelet analysis. IEEE Spectrum, Oct 1996 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley, 2003 J. Devore and R. Peck. Statistics: The Exploration and Analysis of Data. Duxbury Press, 1997. H. Galhardas, D. Florescu, D. Shasha, E. Simon, and C.-A. Saita. Declarative data cleaning: Language, model, and algorithms. VLDB'01 M. Hua and J. Pei. Cleaning disguised missing data: A heuristic approach. KDD'07 H. V. Jagadish, et al., Special Issue on Data Reduction Techniques. Bulletin of the Technical Committee on Data Engineering, 20(4), Dec. 1997 H. Liu and H. Motoda (eds.). Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer Academic, 1998 J. E. Olson. Data Quality: The Accuracy Dimension. Morgan Kaufmann, 2003 D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999 V. Raman and J. Hellerstein. Potters Wheel: An Interactive Framework for Data Cleaning and Transformation, VLDB’2001 T. Redman. Data Quality: The Field Guide. Digital Press (Elsevier), 2001 R. Wang, V. Storey, and C. Firth. A framework for analysis of data quality research. IEEE Trans. Knowledge and Data Engineering, 7:623-640, 1995