Data Mining Concepts and Techniques Chapter 2 Jiawei

Data Mining: Concepts and Techniques — Chapter 2 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign Simon Fraser University © 2012 Han, Kamber, and Pei. All rights reserved. 1

Chapter 2: Getting to Know Your Data n Data Objects and Attribute Types n Basic Statistical Descriptions of Data n Data Visualization n Measuring Data Similarity and Dissimilarity n Summary 2

Data Objects n Data sets are made up of data objects. n A data object represents an entity. n Examples: n n sales database: customers, store items, sales n medical database: patients, treatments n university database: students, professors, courses Also called samples , examples, instances, data points, objects, tuples. n Data objects are described by attributes. n Database rows -> data objects; columns ->attributes. 3

Attributes n Attribute (or dimensions, features, variables): a data field, representing a characteristic or feature of a data object. n n E. g. , customer _ID, name, address Types: n Nominal n Binary n Numeric: quantitative n Interval-scaled n Ratio-scaled 4

Attribute Types n n n Nominal: categories, states, or “names of things” n Hair_color = {auburn, black, blond, brown, grey, red, white} n marital status, occupation, ID numbers, zip codes Binary n Nominal attribute with only 2 states (0 and 1) n Symmetric binary: both outcomes equally important n e. g. , gender n Asymmetric binary: outcomes not equally important. n e. g. , medical test (positive vs. negative) n Convention: assign 1 to most important outcome (e. g. , HIV positive) Ordinal n Values have a meaningful order (ranking) but magnitude between successive values is not known. n Size = {small, medium, large}, grades, army rankings 5

Numeric Attribute Types n n n Quantity (integer or real-valued) Interval n Measured on a scale of equal-sized units n Values have order n E. g. , temperature in C˚or F˚, calendar dates n No true zero-point Ratio n Inherent zero-point n We can speak of values as being an order of magnitude larger than the unit of measurement (10 K˚ is twice as high as 5 K˚). n e. g. , temperature in Kelvin, length, counts, monetary quantities 6

Discrete vs. Continuous Attributes n n Discrete Attribute n Has only a finite or countably infinite set of values n E. g. , zip codes, profession, or the set of words in a collection of documents n Sometimes, represented as integer variables n Note: Binary attributes are a special case of discrete attributes Continuous Attribute n Has real numbers as attribute values n E. g. , temperature, height, or weight n Practically, real values can only be measured and represented using a finite number of digits n Continuous attributes are typically represented as floating-point variables 7

Chapter 2: Getting to Know Your Data n Data Objects and Attribute Types n Basic Statistical Descriptions of Data n Data Visualization n Measuring Data Similarity and Dissimilarity n Summary 8

Basic Statistical Descriptions of Data Motivation n To better understand the data: central tendency, variation and spread n Data dispersion characteristics n median, max, min, quantiles, outliers, variance, etc. n Numerical dimensions correspond to sorted intervals n Data dispersion: analyzed with multiple granularities of precision n Boxplot or quantile analysis on sorted intervals n Dispersion analysis on computed measures n Folding measures into numerical dimensions n Boxplot or quantile analysis on the transformed cube n 9

Measuring the Central Tendency n Mean (algebraic measure) (sample vs. population): Note: n is sample size and N is population size. n n Weighted arithmetic mean: n Trimmed mean: chopping extreme values Median: n Middle value if odd number of values, or average of the middle two values otherwise n n Estimated by interpolation (for grouped data): Mode n Value that occurs most frequently in the data n Unimodal, bimodal, trimodal n Empirical formula: 10

Symmetric vs. Skewed Data n Median, mean and mode of symmetric, positively and negatively skewed data positively skewed June 5, 2021 symmetric negatively skewed Data Mining: Concepts and Techniques 11

Measuring the Dispersion of Data n Quartiles, outliers and boxplots n Quartiles: Q 1 (25 th percentile), Q 3 (75 th percentile) n Inter-quartile range: IQR = Q 3 – Q 1 n Five number summary: min, Q 1, median, Q 3, max n Boxplot: ends of the box are the quartiles; median is marked; add whiskers, and plot outliers individually n n Outlier: usually, a value higher/lower than 1. 5 x IQR Variance and standard deviation (sample: s, population: σ) n Variance: (algebraic, scalable computation) n Standard deviation s (or σ) is the square root of variance s 2 (or σ2) 12

Boxplot Analysis n Five-number summary of a distribution n n Minimum, Q 1, Median, Q 3, Maximum Boxplot n n n Data is represented with a box The ends of the box are at the first and third quartiles, i. e. , the height of the box is IQR The median is marked by a line within the box Whiskers: two lines outside the box extended to Minimum and Maximum Outliers: points beyond a specified outlier threshold, plotted individually 13

Visualization of Data Dispersion: 3 -D Boxplots June 5, 2021 Data Mining: Concepts and Techniques 14

Properties of Normal Distribution Curve n The normal (distribution) curve n From μ–σ to μ+σ: contains about 68% of the measurements (μ: mean, σ: standard deviation) n From μ– 2σ to μ+2σ: contains about 95% of it n From μ– 3σ to μ+3σ: contains about 99. 7% of it 15

Graphic Displays of Basic Statistical Descriptions n Boxplot: graphic display of five-number summary n Histogram: x-axis are values, y-axis repres. frequencies n Quantile plot: each value xi is paired with fi indicating that approximately 100 fi % of data are xi n Quantile-quantile (q-q) plot: graphs the quantiles of one univariant distribution against the corresponding quantiles of another n Scatter plot: each pair of values is a pair of coordinates and plotted as points in the plane 16

Histogram Analysis n n Histogram: Graph display of tabulated frequencies, shown as bars It shows what proportion of cases fall into each of several categories Differs from a bar chart in that it is the area of the bar that denotes the value, not the height as in bar charts, a crucial distinction when the categories are not of uniform width The categories are usually specified as non-overlapping intervals of some variable. The categories (bars) must be adjacent 17

Histograms Often Tell More than Boxplots n The two histograms shown in the left may have the same boxplot representation n n The same values for: min, Q 1, median, Q 3, max But they have rather different data distributions 18

Scatter plot n n Provides a first look at bivariate data to see clusters of points, outliers, etc Each pair of values is treated as a pair of coordinates and plotted as points in the plane 19

Positively and Negatively Correlated Data n The left half fragment is positively correlated n The right half is negative correlated 20

Uncorrelated Data 21

Chapter 2: Getting to Know Your Data n Data Objects and Attribute Types n Basic Statistical Descriptions of Data n Data Visualization n Measuring Data Similarity and Dissimilarity n Summary 22

Data Visualization n Why data visualization? n n n Gain insight into an information space by mapping data onto graphical primitives Provide qualitative overview of large data sets Search for patterns, trends, structure, irregularities, relationships among data Help find interesting regions and suitable parameters for further quantitative analysis Provide a visual proof of computer representations derived 23

Data mapped to geographical space - World oil reserves visualization http: //jordanscience. wordpress. com/2008/08/26/petroleum/ 24

Used by ermission of M. Ward, Worcester Polytechnic Institute Scatterplot Matrices Matrix of scatterplots (x-y-diagrams) of the k-dim. data [total of (k 2/2 -k) scatterplots] 25

Tag Cloud 26

Chapter 2: Getting to Know Your Data n Data Objects and Attribute Types n Basic Statistical Descriptions of Data n Data Visualization n Measuring Data Similarity and Dissimilarity n Summary 27

Similarity and Dissimilarity n n n Similarity n Numerical measure of how alike two data objects are n Value is higher when objects are more alike n Often falls in the range [0, 1] Dissimilarity (e. g. , distance) n Numerical measure of how different two data objects are n Lower when objects are more alike n Minimum dissimilarity is often 0 n Upper limit varies Proximity refers to a similarity or dissimilarity 28

Data Matrix and Dissimilarity Matrix n n Data matrix n n data points with p dimensions n Two modes Dissimilarity matrix n n data points, but registers only the distance n A triangular matrix n Single mode 29

Proximity Measure for Nominal Attributes n n Can take 2 or more states, e. g. , red, yellow, blue, green (generalization of a binary attribute) Method 1: Simple matching n n m: # of matches, p: total # of variables Method 2: Use a large number of binary attributes n creating a new binary attribute for each of the M nominal states 30

Proximity Measure for Binary Attributes Object j n A contingency table for binary data Object i n Distance measure for symmetric binary variables: n Distance measure for asymmetric binary variables: n Jaccard coefficient (similarity measure for asymmetric binary variables): n Note: Jaccard coefficient is the same as “coherence”: 31

Dissimilarity between Binary Variables n Example n n n Gender is a symmetric attribute The remaining attributes are asymmetric binary Let the values Y and P be 1, and the value N 0 32

Dissimilarity between numerical attributes Euclidean Distance 33

Ordinal Variables n An ordinal variable can be discrete or continuous n Order is important, e. g. , rank n Can be treated like interval-scaled n n n replace xif by their rank map the range of each variable onto [0, 1] by replacing i-th object in the f-th variable by compute the dissimilarity using methods for intervalscaled variables 34

Attributes of Mixed Type n n A database may contain all attribute types n Nominal, symmetric binary, asymmetric binary, numeric, ordinal One may use a weighted formula to combine their effects n n n f is binary or nominal: dij(f) = 0 if xif = xjf , or dij(f) = 1 otherwise f is numeric: use the normalized distance f is ordinal n Compute ranks rif and n Treat zif as interval-scaled 35

Cosine Similarity n n A document can be represented by thousands of attributes, each recording the frequency of a particular word (such as keywords) or phrase in the document. Other vector objects: gene features in micro-arrays, … Applications: information retrieval, biologic taxonomy, gene feature mapping, . . . Cosine measure: If d 1 and d 2 are two vectors (e. g. , term-frequency vectors), then cos(d 1, d 2) = (d 1 d 2) /||d 1|| ||d 2|| , where indicates vector dot product, ||d||: the length of vector d 36

Example: Cosine Similarity n n cos(d 1, d 2) = (d 1 d 2) /||d 1|| ||d 2|| , where indicates vector dot product, ||d|: the length of vector d Ex: Find the similarity between documents 1 and 2. d 1 = (5, 0, 3, 0, 2, 0, 0) d 2 = (3, 0, 2, 0, 1, 1, 0, 1) d 1 d 2 = 5*3+0*0+3*2+0*0+2*1+0*1+2*1+0*0+0*1 = 25 ||d 1||= (5*5+0*0+3*3+0*0+2*2+0*0+0*0) 0. 5=(42)0. 5 = 6. 481 ||d 2||=(3*3+0*0+2*2+0*0+1*1+0*0+1*1) 0. 5=(17)0. 5 = 4. 12 cos(d 1, d 2 ) = 0. 94 37

Chapter 2: Getting to Know Your Data n Data Objects and Attribute Types n Basic Statistical Descriptions of Data n Data Visualization n Measuring Data Similarity and Dissimilarity n Summary 38

Summary n Data attribute types: nominal, binary, ordinal, interval-scaled, ratioscaled n Many types of data sets, e. g. , numerical, text, graph, Web, image. n Gain insight into the data by: n Basic statistical data description: central tendency, dispersion, graphical displays n Data visualization: map data onto graphical primitives n Measure data similarity n Above steps are the beginning of data preprocessing. n Many methods have been developed but still an active area of research.

References n n n n n W. Cleveland, Visualizing Data, Hobart Press, 1993 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley, 2003 U. Fayyad, G. Grinstein, and A. Wierse. Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 1990. H. V. Jagadish et al. , Special Issue on Data Reduction Techniques. Bulletin of the Tech. Committee on Data Eng. , 20(4), Dec. 1997 D. A. Keim. Information visualization and visual data mining, IEEE trans. on Visualization and Computer Graphics, 8(1), 2002 D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann, 1999 S. Santini and R. Jain, ” Similarity measures”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9), 1999 E. R. Tufte. The Visual Display of Quantitative Information, 2 nd ed. , Graphics Press, 2001 C. Yu et al. , Visual data mining of multimedia data for social and behavioral studies, Information Visualization, 8(1), 2009