CS910: Foundations
of Data Analytics
Graham Cormode
G.Cormode@warwick.ac.uk
Data Basics
Objectives
 Introduce formal concepts and measures for describing data
 Refresh on relevant concepts from statistics
Distributions, mean and standard deviation, quantiles
– Covariance, Correlation, and correlation tests
–
 Introduce measures of similarity/distance between records
 Understand issues of data quality
–
Techniques for data cleaning, integration, transformation, reduction
Recommended Reading:
 Chapter 2 (Getting to know your data) and Chapter 3 (Data
preprocessing) in "Data Mining: Concepts and Techniques" (Han,
Kamber, Pei).
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CS910 Foundations of Data Analytics
Example Data Set
 Show examples using the “adult census data”
http://archive.ics.uci.edu/ml/machine-learning-databases/adult/
– File: adult.data
–
 Tens of thousands of individuals, one per line
–
Age, Gender, Employment Type, Years of Education…
 Widely studied in Machine Learning community
–
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Prediction task: is income > 50K?
CS910 Foundations of Data Analytics
Digging into Data
 Examine the adult.data set:
39, State-gov, 77516, Bachelors, 13, Never-married, …
 The data is formed of many records
Each record corresponds to an entity in the data: e.g. a person
– May be called tuples, rows, examples, data points, samples
–
 Each record has a number of attributes
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May be called features, columns, dimensions, variables
 Each attribute is one of a number of types
–
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Categoric, binary, numeric, ordered
CS910 Foundations of Data Analytics
Types of Data
 Categoric or nominal attributes take on one of a set of values
Country: England, Mexico, France…;
– Marital status: Single, Divorced, Never-married
– May not be able to “compare” values: only the same or different
–
 Binary attributes take one of two values (often true or false)
An (important) special case of categoric attributes
– Income >50K: Yes/No
– Sex: Male/Female
–
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CS910 Foundations of Data Analytics
Types of Data
 Ordered attributes take values from an ordered set
Education level: high-school, bachelors, masters, phd
 This example is not necessarily fully ordered: is a MD > a phd?
– Coffee size: tall, grande, venti
–
 Numeric attributes measure quantities
Years of education: integer in range 1-16 [in this data set]
– Age: integer in range 17-90
– Could also be real-valued, e.g. temperature 20.5C
–
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CS910 Foundations of Data Analytics
Weka
 A free software tool available to use for data analysis
WEKA: “Waikato Environment for Knowledge Analysis”
– From Waikato University (NZ): www.cs.waikato.ac.nz/ml/weka/
– Open source software in Java for data analysis
–
 Implements many of the ‘core’ algorithms we will be studying
–
Regression, classification, clustering
 Graphical user interface, but also Java implementations
Has some limited visualization options
– Few options for data manipulation inside weka (can do outside)
–
 Prefers to read custom “arff” files: attribute-relation file format
–
Adult.data converted to arff:
www.inf.ed.ac.uk/teaching/courses/dme/data/Adult/adult.arff
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CS910 Foundations of Data Analytics
Metadata
 Metadata = data about the data
–
Describes the data (e.g. by listing the attributes and types)
 Weka uses .arff : Attribute-Relation File Format
Begins by providing metadata before listing the data
– Example (the “Iris” data set):
–
@RELATION iris
@ATTRIBUTE sepallength NUMERIC
@ATTRIBUTE sepalwidth NUMERIC
@ATTRIBUTE petallength NUMERIC
@ATTRIBUTE petalwidth NUMERIC
@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}
@DATA
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
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CS910 Foundations of Data Analytics
The Statistical Lens
 It is helpful to use the tools of statistics to view data
Each attribute can be viewed as describing a random variable
– Look at statistical properties of this random variable
– Can also look at combinations of attributes
 Correlation, Joint and conditional distributions
–
 Distributions from data are typically discrete, not continuous
We study the empirical distribution given by the data
– The event probability is the frequency of occurrences in the data
–
Discrete
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Continuous
CS910 Foundations of Data Analytics
Distributions of Data
 Basic properties of a numeric variable X with observations X1…Xn:
Mean of data, m or E[X] = Si Xi/n: average age in adult.data = 38.6
 Linearity of Expectation: E[X + Y] = E[X] + E[Y] ; E[cX] = cE[X]
– Standard deviation s or √(Var[X]): std. dev. age = 13.64
 Var[X] = E[X2] – (E[X])2 = (Si Xi2)/n – (Si Xi/n)2
 Properties: Var[aX + b] = a2 Var[X]
– Mode: most commonly occurring value
 Most common age in adult.data: 36 (898 examples)
– Median: the midpoint of the distribution
 Half the observations are above, half below
 Mean of the two midpoints for n even
 Median of ages in adult.data = 37
–
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CS910 Foundations of Data Analytics
Probability Distributions
 Given random variable X:
Probability distribution function (PDF), Pr[X = x]
– Cumulative distribution function (CDF), Pr[X ≤ x]
– Complementary Cumulative Distribution Function (CCDF), Pr[X > x]
 Pr[X > x] = 1 – Pr[X ≤ x]
–
 An attribute defines an empirical probability distribution
Pr[X = x] = Si 1[Xi = x]/n [the fraction of examples equal to x]
– E[X] = S Pr[Xi=x] x
– Median(X): x such that Pr[X ≤ x] = 0.5
–
 Conditional probability, Pr[ X | Y ] : probability of X given Y
–
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In data, compute empirically e.g. Pr[Age = 30 | Sex = Female ]
 Compute Pr[Age = 30] only from examples where Sex = Female
CS910 Foundations of Data Analytics
Quantiles
 The quantiles generalize the median
 The f-quantile is the point such that Pr[ X ≤ x] = f
The median is the 0.5 quantile
– The 0-quantile is the minimum, the 1-quantile is the maximum
– The quartiles are the 0.25, 0.5 and 0.75 quantiles
–
 Taking all quantiles at regular intervals (e.g. 0.01)
approximately describes the distribution
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CS910 Foundations of Data Analytics
PDF and CDF of age attribute in adult.data
0.03
0.025
0.02
1
0.015
0.9
0.8
0.01
0.7
0.6
0.005
90
80
70
60
50
40
30
0.4
0.3
(Empirical) PDF of age
0.2
(Empirical) CDF of age
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CS910 Foundations of Data Analytics
90
80
70
60
50
40
30
0
20
0.1
10
0
20
0.5
Skewness in distributions
 Symmetric unimodal distribution:
–
Mean = median = mode
positively skewed
negatively skewed
Age: mean 38.6, median 37, mode 36
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CS910 Foundations of Data Analytics
Statistical Distributions in Data
 Many familiar distributions model observed data
 Normal distribution: characterized by mean m and variance s2
– From μ–σ to μ+σ: contains about 68% of the
measurements (μ: mean, σ: standard deviation)
– From μ–2σ to μ+2σ: contains about 95% of data
 95% of data within 1.96σ of mean
– From μ–3σ to μ+3σ: contains about 99.7% of data
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CS910 Foundations of Data Analytics
Power Law Distribution
 Power law distribution: aka long tail, pareto, zipfian
PDF: Pr[X = x] = c x-a
– CCDF: Pr[X > x] = c’ x1-a
– E[X], Var[X] =  if a < 2
–
 Arise in many cases:
–
–
–
–
–
Number of people living in cities
Popularity of products from retailers
Frequency of word use in written text
Wealth distribution (99% vs 1%)
Video popularity
 Data may also fit a log-normal distribution, truncated power-law
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CS910 Foundations of Data Analytics
Exponential / Geometric Distribution
 Suppose that an event happens with probability p (for small p)
–
Independently at each time step
 How long before an event is observed?
–
Geometric dbn: Pr[X = x] = (1-p)x-1p, x > 0
 CCDF: Pr[ X  x] = (1-p)x
 E[X] = 1/p, Var[X] = (1-p)/p2
 Continuous case: exponential distribution
Pr[X=x] =  exp(- x) for parameter , x  0
– Pr[X  x] = exp(- x)
– E[X] = 1/, Var[X] = 1/2
–
 Both capture “waiting time” between events in Poisson processes
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Memoryless distributions: Pr[X > x + y | X > x] = Pr[X > y]
CS910 Foundations of Data Analytics
Looking at the data
 Simple statistics (mean and variance) roughly describe the data
A Normal distribution is completely characterized by m and s
– But not every data distribution is (approximately) Normal!
– A few large “outlier” values can change the mean by a lot
–
 Looking at a few quantiles helps, but doesn’t tell the whole story
–
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Two distributions can have same quartiles but look quite different!
CS910 Foundations of Data Analytics
Standard plots of data
 There are several basic types of plots of data:
25000
Age versus Education
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Years of Education
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Histogram
(Bar chart)
20000
12
10
15000
8
6
10000
4
2
5000
0
80
90
0
Female
0.03
0.025
0.02
0.015
0.01
90
80
70
60
50
40
30
0
20
0.005
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
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Scatter plot
PDF plot
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Adult data
Male
CDF plot
CS910 Foundations of Data Analytics
90
70
80
60
70
50
Age
60
40
50
30
40
20
30
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Correlation between attributes
 We often want to determine how two attributes behave together
 Scatter plots indicate whether they are correlated:
Positive
Correlation
Positive
Correlation
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Negative
Correlation
Negative
Correlation
CS910 Foundations of Data Analytics
Uncorrelated Data
Age versus Education
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Years of Education
14
12
10
8
6
4
2
0
10
20
30
40
50
60
70
80
90
Age
Adult data
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CS910 Foundations of Data Analytics
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Quantile-quantile plot
 Compare the quantile distribution to another
Allows comparison of attributes of different cardinality
– Plot points corresponding to f-quantile of each attribute
– If close to line y=x, evidence for same distribution
–
 Example: shows q-q plot for sales in two branches
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CS910 Foundations of Data Analytics
Quantile-quantile plot
 Compare years of education in adult.data to adult.test
adult.data: 32K examples, adult.test: 16K examples
– Computed the percentiles of each data set
– Plot corresponding pairs of percentiles
 e.g. using =percentile() function in a spreadsheet
–
adult.test years of education
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16
14
12
10
8
6
4
2
0
0
5
10
15
Adult.data years of education
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CS910 Foundations of Data Analytics
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Measures of Correlation
 Want to measure how correlated are two numeric variables
Covariance of X and Y, Cov(X,Y) = E[(X – E[X])(Y – E[Y])]
= E[ XY – YE[X] – XE[Y] + E[X]E[Y]]
= E[XY] – E[Y]E[X] – E[X]E[Y] + E[X]E[Y]
= E[XY] – E[X]E[Y]
– Notice: Cov(X, X) = E[X2] – E[X]2 = Var(X)
– If X and Y are independent, then E[XY] = E[X]E[Y]: covariance is 0
– But if covariance is 0, X and Y can still be related (dependent)
–
 Consider X =
and Y=X2
–
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x
-2
-1
1
2
Pr[X=x]
0.25
0.25
0.25
0.25
Then E[X] = 0, E[XY] = 0.25(-8 -1 + 1 + 8) =0: covariance is 0
CS910 Foundations of Data Analytics
Example (from past exam)






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E[X] = (8 + 13 + 8 + 8 + 9 + 8 + 9 + 4 + 8 + 5 + 6 + 10)/12 = 8
Var[X] = (0+25+0+0+1+0+1+16+0+9+4+4)/12 = 5
E[YZ] = (56+180+49+42+96+24+72+9+42+18+20+160)/12 = 64
E[Z] = (8 + 10 + 7 + 7 + 8 + 6 + 8 + 3 + 6 + 6 + 5 + 10)/12 = 7
E[Y] = (7 + 18 + 7 + 6 + 12 + 4 + 9 + 3 + 7 + 3 + 4 + 16)/12 = 8
Cov(Y,Z) = (E[YZ] - E[Y]E[Z]) = 64 - (7 * 8) = 8
CS910 Foundations of Data Analytics
Measuring Correlation
 Covariance depends on the magnitude of values of variables
Cov(aX,bY) = ab Cov(X,Y), proved by linearity of expectation
– Normalize to get a measure of the amount of correlation
–
 Pearson product-moment correlation coefficient (PMCC)
PMCC(X,Y) = Cov(X,Y)/(s(X) s(Y))
= (E[XY] – E[X]E[Y]) / √(E[X2] – E2[X]) √(E[Y2]-E2[Y])
= (ni xi yi – (i xi)(i yi))/√(ni xi2 – (i xi)2)√(ni yi2 – (i yi)2)
– Measures linear dependence in terms of simple quantities:
 n, number of examples (assumed to be reasonably large)
 i xi yi, sum of products
 i xi, i yi, sum of values
 i xi2, i yi2, sum of squares
–
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CS910 Foundations of Data Analytics
Interpreting PMCC
 In what range does PMCC fall?
–
–
–
–
–
–
–
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Assume E[X] = 0, E[Y] = 0
(as we can “shift” distribution without changing covariance)
Set X’ = X/s(X), and Y’ = Y/s(Y)
Var(X’) = Var(X)/s2(X) = 1, E[X’] = 0, Var(Y’) = 1, E[Y’] = 0
E[ (X’-Y’)2 ]  0, since (X’-Y’)2  0
E[ X’2 + Y’2 – 2X’Y’]  0
2E[X’Y’]  E[X’]2 + E[Y’]2 = 2
E[X’Y’]  1
Rescaling, E[XY] = s(X) s(Y) E[X’Y’]  s(X)s(Y)
Similarly, -s(X)s(Y)  E[XY]
Hence, -1  PMCC(X,Y)  +1
CS910 Foundations of Data Analytics
Interpreting PMCC
 Suppose X = Y [perfectly linearly correlated]
–
PMCC(X,Y) = (E[XY] – E[X]E[Y])/(s(X)s(Y))
= (E[X2] – E2[X])/s2(X)
= Var(X)/Var(X) = 1
 Suppose X = -aY + b [perfectly negatively linearly correlated]
–
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PMCC(X,Y) = (E[-aYY + bY] – E[-aY + b]E[Y])/s(-aY + b)s(Y)
= -aE[Y2] + bE[Y] – bE[Y] + aE2[Y])/(as2(Y))
= -aVar(Y)/(aVar(Y)) = -1
CS910 Foundations of Data Analytics
Example of PMCC
What kind of correlation is there between Y and Z?
 Cov(Y,Z) = 8 [from previous slide]
 Var[Y] = 22.5, Var[Z] = 3.666 [check at your leisure]
 PMCC(Y,Z) = 8/ √(22.5 * 3.66) = 0.88…
 Correlation between Y and Z is strong, positive, linear
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CS910 Foundations of Data Analytics
Correlation for Ordered Data
 What about when data is ordered, but not numeric?
–
Can look at how the ranks of the variables are correlated
 Example: (Grade in Mathematics, Grade in Statistics):
Data: (A, A), (B, C), (C, E), (F, F)
– Convert to ranks: (1, 1), (2, 2), (3, 3), (4, 4)
– Ranks are perfectly correlated
–
 Use PMCC on the ranks
Obtains Spearman’s Rank Correlation Coefficient (RCC)
– For ties, define rank as mean position in sorted order
– Also useful for identifying non-linear correlation
 Y = X2 has RCC(X,Y) = 1
–
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CS910 Foundations of Data Analytics
Testing Correlation in Categoric Data
 Test for statistical significance of correlation in categoric data
 Look at how many times pairs co-occur, compared to expectation
Consider attributes X with values x1 … xc, Y with values y1 … yr
– Let oij be the number of pairs (xi, yj)
– Let eij be the expected number if independent, = n Pr[X=xi] Pr[Y=yj]
–
 Pearson 2 (chi-squared) statistic:
–
2(X,Y) = i j (oij – eij)2/eij
 Compare 2 test statistic with (r-1)(c-1) degrees of freedom
Suppose r = c = 10, 2 for 81 d.o.f with 0.01 confidence = 113.51
– If 2(X,Y) > 113.51 can conclude X and Y are (likely) correlated
 Otherwise, there is not evidence to support this conclusion
–
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CS910 Foundations of Data Analytics
Chi-squared example (from textbook)
Male
Female
Total
Fiction
250 (90)
200 (360)
450
Non-fiction
50 (210)
1000 (840)
1050
Total
300
1200
1500
Is gender and preferred reading material correlated?
 Expected frequency (male, fiction) = (300 * 450)/1500 = 90
 2 = (250-90)2/90 + (50-210)2/210 + (200-360)2/360 + (1000-840)2/840
= 507.93
 Degrees of freedom = (2-1)(2-1) = 1
 Test statistics for 1 DoF = 10.828 [lookup in table]
–
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Reject that they are independent, conclude that there is correlation
CS910 Foundations of Data Analytics
Data Similarity Measures
 Need ways to measure similarity or dissimilarity of data points
Within various analytic methods: clustering, classification
– Many different ways are used in practice
–
 Typically, measure dissimilarity of corresponding attribute values
Combine all these to get overall dissimilarity / distance
– Distance = 0: identical
– Increasing distance: less alike
–
 Typically look for distances that obey the metric rules:
d(x,x) = 0: Identity rule
– d(x, y) = d(y, x): Symmetric rule
– d(x, y) + d(y, z)  d(x, z): Triangle inequality
–
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CS910 Foundations of Data Analytics
Categoric data
 Suppose data has all categoric attributes
 Measure dissimilarity by number of differences
Sometimes called “Hamming distance”
– Example:
(Private, Bachelors, England, Male)
(Private, Masters, England, Female)
2 differences, so distance is 2
– Can encode into binary vectors (useful for later):
High-school = 100, Bachelors = 010, Masters = 001
–
 May build your own custom score functions
–
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Example:
d(Bachelors, Masters) = 0.5, d(Masters, High-school) = 1,
d(Bachelors, High-school) = 0.5
CS910 Foundations of Data Analytics
Binary data
 Suppose data has all binary attributes
–
Could count number of differences again (Hamming distance)
 Sometimes, “True” is more significant than “False”
E.g. presence of a medical symptom
– Then only consider cases where one attribute is True
 Called “Jaccard distance”
– Measure fraction of disagreeing cases (0…1)
– Example:
(Cough=T, Fever=T, Tired=F, Nausea=F)
(Cough=T, Fever=F, Tired=T, Nausea=F)
Fraction of disagreeing cases = 2/3
–
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CS910 Foundations of Data Analytics
Numeric Data
 Suppose all data has numeric attributes
–
Can interpret data points as coordinates
 Measure distance between points with appropriate distances
Euclidean distance (L2): d(x,y) = ǁx-yǁ2 = √i (xi – yi)2
– Manhattan distance (L1): d(x,y)= ǁx-yǁ1 = i|xi – yi| [absolute values]
– Maximum distance (L): d(x,y) = ǁx-yǁ = Maxi |xi – yi|
– [Examples of Minkowski distances (Lp): ǁx -yǁp = (i (xi – yi)p)1/p ]
–
 If ranges of values are vastly different, may normalize first
Range scaling: Rescale so all values lie in the range [0…1]
– Statistical scaling: Subtract mean, divide by standard deviation
 Obtain the z-score, (x – μ)/σ
–
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CS910 Foundations of Data Analytics
Ordered Data
 Suppose all data is ordered data
Can replace each point with its position in the ordering
– Example: tall = 1, grande = 2, venti = 3
–
 Measure L1 distance of this encoding of the data
–
d(tall, venti) = 2
 May also normalize so distances are in range [0…1]
–
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tall = 0, grande = 0.5, venti = 1
CS910 Foundations of Data Analytics
Mixed data
 But most data is a mixture of different types!
 Encode each dimension into the range [0…1], use Lp distance
–
–
–
–
–
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Following previous techniques
(Age: 36, coffee: tall, education: bachelors, sex: male)
(0.36, 0, 010, 0)
(Age: 46, coffee: grande, education: masters, sex: female)
(0.46, 0.5, 001, 1)
L1 distance: 0.1 + 0.5 + 2 + 1 = 3.6
L2 distance: √(0.01 + 0.25 + 4 + 1) = √(5.26) = 2.29
May reweight some coordinates to make more uniform
 E.g. weight education by 0.5
CS910 Foundations of Data Analytics
Cosine Similarity
 For large vector objects, cosine similarity is often used
E.g. in measuring similarity of documents
– Each coordinate indicates how often a word occurs in the document
“to be or not to be” : [to: 2, be: 2, or: 1, not: 1, artichoke: 0…]
–
 Similarity between two vectors is given by x  y/ǁxǁ2 ǁyǁ2
(x  y) = i (xi * yi) : dot product of x and y
– ǁxǁ2 = ǁx - 0ǁ2 = √(i xi2) : Euclidean norm of x
–
 Example: “to be or not to be”, “do be do be do”
Cosine similarity: 4/√10 √13 = 0.35
 Example: “to be or not to be”, “to not be or to be”
Cosine similarity: 10/ √10 √10 = 1.0
 Example: “to be or not to be”, “that is the question”
Cosine similarity: 0/ √10 √4 = 0.0
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CS910 Foundations of Data Analytics
(Text) Edit Distance
 Edit Distance: Count the number of inserts, deletes and changes
of characters to turn string A into string B
–
Seems like there could be many ways to do this
 Computer science: Use dynamic programming
Let Ai be the first i characters of string A, A[i] is i’th character of A
– Use a recurrence formula:
d(Ai, “”) = i and d(“”, Bj) = j
d(Ai, Bj) = min{d(Ai-1,Bj-1) + (A[i]≠B[j]),
d(Ai-1, Bj) +1,
d(Ai,Bj-1) + 1}
where (A[i]≠B[j]) is 0 if i’th character
of A matches j’th character of B, else 1
– Can compute d(A,B) by filling a grid
–
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CS910 Foundations of Data Analytics
Data Preprocessing
 Often we need to preprocess the data before analysis
Data cleaning: remove noise, correct inconsistencies
– Data integration: merge data from multiple sources
– Data reduction: reduce data size for ease of processing
– Data transformation: convert to different scales (e.g. normalization)
–
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CS910 Foundations of Data Analytics
Data Cleaning – Missing Values
 Missing values are common in data
–
Many instances of ? in adult.data
 Values can be missing for many reasons
Data was lost / transcribed incorrectly
– Data could not be measured
– Did not apply in context: “not applicable” (e.g. national ID number)
– User chose not to reveal some private information
–
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CS910 Foundations of Data Analytics
Handling Missing Values
 Drop the whole record
OK if a small fraction of records have missing values
2400 rows in adult.data have a ?, out of 32K: 7.5%
– Not ideal if missing values correlate with other features
–
 Fill in missing values manually
Based on human expertise
– Would you want to look through 2400 examples?
–
 Accept missing values as “unknown”
Need to ensure that future processing can handle “unknown”
– May lead to false patterns being found: a cluster of “unknown”s
–
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CS910 Foundations of Data Analytics
Handling Missing Values
 Fill in some plausible value based on the data
E.g. for a missing temperature, fill in the mean temperature
– E.g. for a missing education level, fill in most common one
– May be the wrong thing to do if missing value means something
–
 Use the rest of the data to infer the missing value
Find a value that looks like best fit given other values in record
 E.g., mean value of those that match on another attribute
– Build a classifier to predict the missing value
– Use regression to extrapolate the missing value
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 No ideal solution – all methods introduce some bias…
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CS910 Foundations of Data Analytics
Noise in Data
 “Noise” is values due to error or variance in measurement
Random noise in measurements e.g. in temperature, time
– Interference or misconfiguration of devices
– Coding / translation errors in software
– Misleading values from data subjects
 E.g. Date of birth = 1/1/1970
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 Noise can be difficult to detect at the record level
If salary is 64,000 instead of 46,000, how can you tell?
– Statistical tests may help identify if there is much noise in data
 Do many people in data make more salary than national avg?
 Benford’s law: dbn of first digits is skewed, Pr[d] ≈ log(1 + 1/d)
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CS910 Foundations of Data Analytics
Outliers
 Outliers are extreme values in data that often represent noise
E.g. salary = -10,000 [hard constraint: no salary is negative]
– E.g. room temperature = 100C [constraint: should be below 40C?]
– Is salary = $1M an outlier? What about salary = $1?
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 Finding outliers in numeric data:
Sanity check: is mean, std dev, max, much higher than expected?
– Visual: plot the data, are there spikes or values far from rest?
– Rule-based: set limits, declare an outlier if outside bounds
– Data-based: declare an outlier if > 6 standard deviations from mean
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CS910 Foundations of Data Analytics
Outliers
 Finding outliers in categoric data:
Visual: Look at frequency statistics / histogram
– Are there values with low frequency representing typos/errors?
 E.g. Mela instead of Male
 Values other than True or False in Binary data
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 Dealing with outliers
Delete outliers: remove records with outlier values from data set
– Clip outliers: change value to maximum/minimum permitted
– Treat as missing value: replace with more typical / plausible value
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Outliers
0.03
0.025
0.02
0.015
0.01
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90
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40
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0
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0.005
GENDER Frequency
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F 12
M 13
X1
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CS910 Foundations of Data Analytics
Consistency Rules
 Data may be inconsistent in a number of ways
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US vs European date styles: 17/10 vs 10/17
Temperature in Fahrenheit instead of Centrigrade
Same concept represented in multiple ways: tall, TALL, small?
Typos: age = 337
Functional dependencies: Annual salary = 52K, Weekly salary = 1500
Address does not exist
 Apply rules and use tools
Spell correction / address standardization tools
– (Manually) find “consistent inconsistencies” and fix with a script
– Look for “minimal repair”: smallest change that makes consistent
 Principle of Parsimony / Occam’s Razor
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CS910 Foundations of Data Analytics
Data Integration
 Want to combine data from two sources
May be inconsistent: different units/formats
– May structurally differ: address vs (street number, road name)
– May be different name for same entity: B. Obama vs Pres. Obama
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 Challenging problem faced by many organizations
E.g. two companies merge and need to combine databases
– Identify corresponding attributes by correlation analysis
– Define rules to translate between formats
– Try to identify matching entities within data via similarity/distance
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CS910 Foundations of Data Analytics
Data Transformation
– Sometimes we need to transform the data before it can be used
 Some methods want a small number of categoric values
 Sometimes methods expect all values to be numeric
 Sometimes need to reweight/rescale data so all features equal
 Have seen several data transformations already:
 Represent ordered data by numbers/ranks
 Normalize numeric values by range scaling or divide by variance
 Another important transformation: discretization
 Turn a fine-grained attribute into a coarser one
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CS910 Foundations of Data Analytics
Discretization
 Features have too many values to be informative
E.g. everyone has a different salary
– E.g. every location is at a different GPS location
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 Can coarsen data to create fewer, well-supported groups
Binning: Place numeric/ordered data into bands e.g. salaries, ages
 Based on domain knowledge: Ages 0-18, 18-24, 25-34, 35-50…
 Based on data distribution: partition by quantiles
– Use existing hierarchies in data
 Time in second/minute/hour/day/week/month/year…
 Geography in postcode (CV4 7AL), town/region (CV4), county…
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CS910 Foundations of Data Analytics
Data Reduction
 Sometimes data is too large to conveniently work with
Too high dimensional: too many attributes to make sense of
– Too numerous: too many examples to process
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 Complex analytics may take a long time for large data
Painful when trying many different approaches/parameters
– Can we draw almost the same conclusions with smaller data?
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CS910 Foundations of Data Analytics
Random Sampling for number reduction
 A solid principle from statistics: a random sample is often enough
Pick a subset of rows from data (sampling without replacement)
– Must be chosen randomly
 Suppose data sorted by some attribute (salary):
conclusions would be biased
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 A standard trick: add an extra random field to data, and sort on it
 Sampling can miss out small subpopulations
Stratified sampling: take samples from subsets
– E.g. sample from each occupation in adult.data
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CS910 Foundations of Data Analytics
Feature selection (dimensionality reduction)
Feature selection picks a subset of attributes
 Transformations of numeric data
Numeric data: all n features are numeric, so data is a set of vectors
– Fourier (or other) transform and pick important coefficients
– May make it hard to interpret the new attributes
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(Discrete) Fourier transform
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CS910 Foundations of Data Analytics
Feature selection
 Principal Components Analysis (also on numeric data)
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Pick k < n orthogonal vectors to project the data along
The projection of data x on direction y is dot product (x . y)
Gives a new set of coordinate axes for the data
PCA procedure finds the “best” set of directions
Still challenging to interpret the new axes
CS910 Foundations of Data Analytics
Feature selection
 Greedy attribute selection
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Pick first attribute that contains most “information”
Add more attributes that maximize information (forward search)
Based on statistical tests or information theory
Or, start with all attributes and start removing (backward search)
May not reach the same set of attributes!
Input set of attributes: {A1, A2, A3, A4, A5, A6}
Forward search: {}  {A1}  {A1, A6}  {A1, A4, A6}
Backward search: {A1, A2, A3, A4, A5, A6}  {A1, A3, A4, A5, A6} 
{A1, A3, A4, A6}  {A1, A3, A6}
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CS910 Foundations of Data Analytics
Implementing feature selection
 Previous methods implemented in some tools for analysis
Packages in R
– Methods in Weka
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 Simple techniques for feature selection:
High correlation: is a variable very highly correlated with another?
 If so, it can likely be discarded
– Try your analysis with all combinations of features for small input
 Exponentially expensive, so may defeat the point!
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CS910 Foundations of Data Analytics
Data Reduction by Aggregation
 Aggregate individual records together to get weighted data
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E.g. look at total trading volume per minute from raw trades
 Aggregate and coarsen within feature hierarchies
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E.g. look at (average) share price per hour, not per minute
 Look at a meaningful subset of the data
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E.g. process only 1 month’s data instead of 1 year
Industry
Region
Year
Category Country Quarter
Product
City Month
Office
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Week
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CS910 Foundations of Data Analytics
Summary of Data Basics
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Data can be broken down into records with attributes
Statistical tools describe the distribution of attributes
Powerful tools for finding correlation, similarity/distance
Many problems with data, no perfect solutions
Recommended Reading:
 Chapter 2 (Getting to know your data) and Chapter 3 (Data
preprocessing) in "Data Mining: Concepts and Techniques"
(Han, Kamber, Pei).
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CS910 Foundations of Data Analytics