Lec01-INTRO TO DATA MINING.pdf

8/28/24 DATA MINING OBJECTIVES: Provide a review of Data Science or Data Analytics Understand the importance of Data Preprocessing Apply the different data pre-processing...

8/28/24 DATA MINING OBJECTIVES: Provide a review of Data Science or Data Analytics Understand the importance of Data Preprocessing Apply the different data pre-processing 1  8/28/24 Data vs Information Data Facts and statistics collected together for reference or analysis. Things known or assumed as facts, making the basis of reasoning or calculation. Information What is conveyed or represented by a particular arrangement or sequence of things. Data as processed, stored, or transmitted by a computer. Types of Data Qualitative Data Quantitative Data 2  8/28/24 Website upload/download speed Conversion rate Computer Assisted Personal Interview 54% people prefer shopping online instead of going to the mall Better standard of living Home schooling over traditional schooling 3  8/28/24 Levels of Measurement Scale of Measurement Used to describe information within the values Nominal Ordinal Interval Ratio Nationality Level of service Annual sales Educational level IQ test Hair color Voltage Crime rate Height 4  8/28/24 Types of Dataset Record Graph Ordered Data Time Series The six Vs of Big Data 5  8/28/24 Application Domains 6  8/28/24 Foundations of Data Analytics Taxonomy of Data Analytics Descriptive Analytics – summarize or condenses data to extract patterns Predictive Analytics – extracts models from data to be used for future predictions Diagnostic Analytics – diagnose various problems that are exhibited through data Prescriptive Analytics – combines insights from the first three which allows companies to make decisions based on them Cognitive Analytics – unfold hidden patterns and replicate human thought 7  8/28/24 Taxonomy of Data Analytics Data Analytics Framework 8  8/28/24 Descriptive Analytics Descriptive Analytics or Exploratory Data Analysis - data is described and summarized using basic statistical tools and graphs to produce reports and dashboards for decision making Predictive Analytics Algorithms Supervised Learning Classification Regression Time Series Analysis Unsupervised Learning Clustering Association Analysis Sequential Pattern Analysis Text Mining/Social Media Sentiment Analysis 9  8/28/24 Prescriptive Analytics Prescriptive Analytics or Optimization is an application of analytics that recommends the optimal solution to a problem given constraints. This application also seeks to find the best solution given multiple what-if scenarios Data Quality: Why Preprocess the Data? 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 understood? 10  8/28/24 Major Tasks in Data Preprocessing Data cleaning Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration 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 Data Integration Data integration: Combines data from multiple sources into a coherent store Schema integration: e.g., A.cust-id ≡ B.cust-# 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 11  8/28/24 Handling Redundancy in Data Integration Redundant data occur often during the 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 detected by correlation analysis and covariance analysis Careful integration of the data from multiple sources may help reduce/ avoid redundancies and inconsistencies and improve mining speed and quality 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 12  8/28/24 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 13  8/28/24 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. rA,B = 0: independent; rAB < 0: negatively correlated 14  8/28/24 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 15  8/28/24 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 imply independence 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. 16  8/28/24 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 17  8/28/24 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) 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 18  8/28/24 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 components, it is possible to reconstruct a good approximation of the original data) Works for numeric data only 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 19  8/28/24 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 Optimal branch and bound: Use attribute elimination and backtracking Attribute Creation (Feature Generation) Create new attributes (features) that can capture the important information in a data set more effectively than the original ones general methodologies Attribute extraction Domain-specific Attribute construction Combining features Data discretization 20  8/28/24 Numerosity Reduction Reduce data volume by choosing an 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 21  8/28/24 Regression Analysis y Y1 Regression analysis: A collective name for techniques for the modeling and analysis of numerical data consisting of values of a dependent variable (also called response variable or Y1’ y=x+1 measurement) and of one or more independent variables (aka. explanatory variables or predictors) X1 x 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), squares method, but other criteria have also been used inference, hypothesis testing, and modeling of causal relationships Regression Analysis and Log-Linear 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 multi-dimensional space for a set of discretized attributes, based on a smaller subset of dimensional combinations Useful for dimensionality reduction and data smoothing 22  8/28/24 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) 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 23  8/28/24 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 (page at a time) 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 24  8/28/24 Sampling: With or without Replacement WOR SRS le random p t (sim le withou samp ement) c repla SRSW R Raw Data Sampling: Cluster or Stratified Sampling 25  8/28/24 Data Cube Aggregation The lowest level of a data cube (base cuboid) The aggregated data for an individual entity of interest E.g., a customer in a phone calling data warehouse Multiple levels of aggregation in data cubes Further reduce the size of data to deal with Reference appropriate levels Use the smallest representation which is enough to solve the task Queries regarding aggregated information should be answered using data cube, when possible 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 be considered as forms of data compression 26  8/28/24 Data Transformation A function that maps the entire set of values of a given attribute to a new set of replacement values such that 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 Discretization: Concept hierarchy climbing Normalization Min-max normalization: to [new_minA, new_maxA] Ex. 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): Ex. Let μ = 54,000, σ = 16,000. Then Normalization by decimal scaling Where j is the smallest integer such that Max(|ν’|) < 1 27  8/28/24 Normalization Normalization by decimal scaling Where j is the smallest integer such that Max(|ν’|) < 1 Suppose that the recorded values of A range from -986 to 917. The maximum absolute value of A is 986. To normalize by decimal scaling, we, therefore, divide each value by 1000 so that -986 normalizes to -0.986 and 917 normalizes to 0.917. 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 Prepare for further analysis, e.g., classification 28  8/28/24 Data Discretization Methods Typical methods: All the methods can be applied recursively Binning Top-down split, unsupervised Histogram analysis Top-down split, unsupervised Clustering analysis (unsupervised, top-down split or bottom-up merge) Decision-tree analysis (supervised, top-down split) Correlation (e.g., χ2) analysis (unsupervised, bottom-up merge) 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 Managing categorical attributes can be tricky 29  8/28/24 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 street < city < state < country 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 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, country} 30  8/28/24 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 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 31  8/28/24 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 32