There are questions in the attached word document for which I need answers. There is also a PPT attached for references
ITS632 Intro to Data Mining
Dr. Patrick Haney
Dept. of Information Technology &
School of Computer and Information Sciences
University of the Cumberlands
Chapter 2 Assignment
1. What’s an attribute? What’s a data instance?
2. What’s noise? How can noise be reduced in a dataset?
3. Define outlier. Describe 2 different approaches to detect outliers in a dataset.
4. Describe 3 different techniques to deal with missing values in a dataset. Explain when each of these techniques would be most appropriate.
5. Given a sample dataset with missing values, apply an appropriate technique to deal with them.
6. Give 2 examples in which aggregation is useful.
7. Given a sample dataset, apply aggregation of data values.
8. What’s sampling?
9. What’s simple random sampling? Is it possible to sample data instances using a distribution different from the uniform distribution? If so, give an example of a probability distribution of the data instances that is different from uniform (i.e., equal probability).
10. What’s stratified sampling?
11. What’s “the curse of dimensionality”?
12. Provide a brief description of what Principal Components Analysis (PCA) does. [Hint: See Appendix A and your lecture notes.] State what’s the input and what the output of PCA is.
13. What’s the difference between dimensionality reduction and feature selection?
14. Describe in detail 2 different techniques for feature selection.
15. Given a sample dataset (represented by a set of attributes, a correlation matrix, a covariance matrix, …), apply feature selection techniques to select the best attributes to keep (or equivalently, the best attributes to remove).
16. What’s the difference between feature selection and feature extraction?
17. Give two examples of data in which feature extraction would be useful.
18. Given a sample dataset, apply feature extraction.
19. What’s data discretization and when is it needed?
20. What’s the difference between supervised and unsupervised discretization?
21. Given a sample dataset, apply unsupervised (e.g., equal width, equal frequency) discretization, or supervised discretization (e.g., using entropy).
22. Describe 2 approaches to handle nominal attributes with too many values.
23. Given a dataset, apply variable transformation: Either a simple given function, normalization, or standardization.
24. Definition of Correlation and Covariance, and how to use them in data preprocessing (see pp. 7678).
Data Mining: Data
Lecture Notes for Chapter 2
Introduction to Data Mining
by
Tan, Steinbach, Kumar
What is Data?
Collection of data objects and their attributes
An attribute is a property or characteristic of an object
Examples: eye color of a person, temperature, etc.
Attribute is also known as variable, field, characteristic, or feature
A collection of attributes describe an object
Object is also known as record, point, case, sample, entity, or instance
Attributes
Objects
Attribute Values
Attribute values are numbers or symbols assigned to an attribute
Distinction between attributes and attribute values
Same attribute can be mapped to different attribute values
Example: height can be measured in feet or meters
Different attributes can be mapped to the same set of values
Example: Attribute values for ID and age are integers
But properties of attribute values can be different
ID has no limit but age has a maximum and minimum value
Measurement of Length
The way you measure an attribute is somewhat may not match the attributes properties.
Types of Attributes
There are different types of attributes
Nominal
Examples: ID numbers, eye color, zip codes
Ordinal
Examples: rankings (e.g., taste of potato chips on a scale from 110), grades, height in {tall, medium, short}
Interval
Examples: calendar dates, temperatures in Celsius or Fahrenheit.
Ratio
Examples: temperature in Kelvin, length, time, counts
Properties of Attribute Values
The type of an attribute depends on which of the following properties it possesses:
Distinctness: =
Order: < >
Addition: + –
Multiplication: * /
Nominal attribute: distinctness
Ordinal attribute: distinctness & order
Interval attribute: distinctness, order & addition
Ratio attribute: all 4 properties
Attribute Type
Description
Examples
Operations
Nominal
The values of a nominal attribute are just different names, i.e., nominal attributes provide only enough information to distinguish one object from another. (=, )
zip codes, employee ID numbers, eye color, sex: {male, female}
mode, entropy, contingency correlation, 2 test
Ordinal
The values of an ordinal attribute provide enough information to order objects. (<, >)
hardness of minerals, {good, better, best},
grades, street numbers
median, percentiles, rank correlation, run tests, sign tests
Interval
For interval attributes, the differences between values are meaningful, i.e., a unit of measurement exists.
(+, – )
calendar dates, temperature in Celsius or Fahrenheit
mean, standard deviation, Pearson’s correlation, t and F tests
Ratio
For ratio variables, both differences and ratios are meaningful. (*, /)
temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical current
geometric mean, harmonic mean, percent variation
Attribute Level
Transformation
Comments
Nominal
Any permutation of values
If all employee ID numbers were reassigned, would it make any difference?
Ordinal
An order preserving change of values, i.e.,
new_value = f(old_value)
where f is a monotonic function.
An attribute encompassing the notion of good, better best can be represented equally well by the values {1, 2, 3} or by { 0.5, 1, 10}.
Interval
new_value =a * old_value + b where a and b are constants
Thus, the Fahrenheit and Celsius temperature scales differ in terms of where their zero value is and the size of a unit (degree).
Ratio
new_value = a * old_value
Length can be measured in meters or feet.
Discrete and Continuous Attributes
Discrete Attribute
Has only a finite or countably infinite set of values
Examples: zip codes, counts, or the set of words in a collection of documents
Often represented as integer variables.
Note: binary attributes are a special case of discrete attributes
Continuous Attribute
Has real numbers as attribute values
Examples: temperature, height, or weight.
Practically, real values can only be measured and represented using a finite number of digits.
Continuous attributes are typically represented as floatingpoint variables.
Types of data sets
Record
Data Matrix
Document Data
Transaction Data
Graph
World Wide Web
Molecular Structures
Ordered
Spatial Data
Temporal Data
Sequential Data
Genetic Sequence Data
Important Characteristics of Structured Data
Dimensionality
Curse of Dimensionality
Sparsity
Only presence counts
Resolution
Patterns depend on the scale
Record Data
Data that consists of a collection of records, each of which consists of a fixed set of attributes
Tid
Refund
Marital
Status
Taxable
Income
Cheat
1
Yes
Single
125K
No
2
No
Married
100K
No
3
No
Single
70K
No
4
Yes
Married
120K
No
5
No
Divorced
95K
Yes
6
No
Married
60K
No
7
Yes
Divorced
220K
No
8
No
Single
85K
Yes
9
No
Married
75K
No
10
No
Single
90K
Yes
10
Data Matrix
If data objects have the same fixed set of numeric attributes, then the data objects can be thought of as points in a multidimensional space, where each dimension represents a distinct attribute
Such data set can be represented by an m by n matrix, where there are m rows, one for each object, and n columns, one for each attribute
Document Data
Each document becomes a `term’ vector,
each term is a component (attribute) of the vector,
the value of each component is the number of times the corresponding term occurs in the document.
Document 1�
season�
timeout�
lost�
win�
game�
score�
ball�
play�
coach�
team�
Document 2�
Document 3�
3�
0�
5�
0�
2�
6�
0�
2�
0�
2�
0�
0�
7�
0�
2�
1�
0�
0�
3�
0�
0�
1�
0�
0�
1�
2�
2�
0�
3�
0�
Transaction Data
A special type of record data, where
each record (transaction) involves a set of items.
For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.
TID
Items
1
Bread, Coke, Milk
2
Beer, Bread
3
Beer, Coke, Diaper, Milk
4
Beer, Bread, Diaper, Milk
5
Coke, Diaper, Milk
Graph Data
Examples: Generic graph and HTML Links
Chemical Data
Benzene Molecule: C6H6
Ordered Data
Sequences of transactions
An element of the sequence
Items/Events
Ordered Data
Genomic sequence data
Ordered Data
SpatioTemporal Data
Average Monthly Temperature of land and ocean
Data Quality
What kinds of data quality problems?
How can we detect problems with the data?
What can we do about these problems?
Examples of data quality problems:
Noise and outliers
missing values
duplicate data
Noise
Noise refers to modification of original values
Examples: distortion of a person’s voice when talking on a poor phone and “snow” on television screen
Two Sine Waves
Two Sine Waves + Noise
Outliers
Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set
Missing Values
Reasons for missing values
Information is not collected
(e.g., people decline to give their age and weight)
Attributes may not be applicable to all cases
(e.g., annual income is not applicable to children)
Handling missing values
Eliminate Data Objects
Estimate Missing Values
Ignore the Missing Value During Analysis
Replace with all possible values (weighted by their probabilities)
Duplicate Data
Data set may include data objects that are duplicates, or almost duplicates of one another
Major issue when merging data from heterogeous sources
Examples:
Same person with multiple email addresses
Data cleaning
Process of dealing with duplicate data issues
Data Preprocessing
Aggregation
Sampling
Dimensionality Reduction
Feature subset selection
Feature creation
Discretization and Binarization
Attribute Transformation
Aggregation
Combining two or more attributes (or objects) into a single attribute (or object)
Purpose
Data reduction
Reduce the number of attributes or objects
Change of scale
Cities aggregated into regions, states, countries, etc
More “stable” data
Aggregated data tends to have less variability
Aggregation
Standard Deviation of Average Monthly Precipitation
Standard Deviation of Average Yearly Precipitation
Variation of Precipitation in Australia
Sampling
Sampling is the main technique employed for data selection.
It is often used for both the preliminary investigation of the data and the final data analysis.
Statisticians sample because obtaining the entire set of data of interest is too expensive or time consuming.
Sampling is used in data mining because processing the entire set of data of interest is too expensive or time consuming.
Sampling …
The key principle for effective sampling is the following:
using a sample will work almost as well as using the entire data sets, if the sample is representative
A sample is representative if it has approximately the same property (of interest) as the original set of data
Types of Sampling
Simple Random Sampling
There is an equal probability of selecting any particular item
Sampling without replacement
As each item is selected, it is removed from the population
Sampling with replacement
Objects are not removed from the population as they are selected for the sample.
In sampling with replacement, the same object can be picked up more than once
Stratified sampling
Split the data into several partitions; then draw random samples from each partition
Sample Size
8000 points 2000 Points 500 Points
Sample Size
What sample size is necessary to get at least one object from each of 10 groups.
Curse of Dimensionality
When dimensionality increases, data becomes increasingly sparse in the space that it occupies
Definitions of density and distance between points, which is critical for clustering and outlier detection, become less meaningful
Randomly generate 500 points
Compute difference between max and min distance between any pair of points
Dimensionality Reduction
Purpose:
Avoid curse of dimensionality
Reduce amount of time and memory required by data mining algorithms
Allow data to be more easily visualized
May help to eliminate irrelevant features or reduce noise
Techniques
Principle Component Analysis
Singular Value Decomposition
Others: supervised and nonlinear techniques
Dimensionality Reduction: PCA
Goal is to find a projection that captures the largest amount of variation in data
x2
x1
e
Dimensionality Reduction: PCA
Find the eigenvectors of the covariance matrix
The eigenvectors define the new space
x2
x1
e
Dimensionality Reduction: ISOMAP
Construct a neighbourhood graph
For each pair of points in the graph, compute the shortest path distances – geodesic distances
By: Tenenbaum, de Silva, Langford (2000)
Dimensionality Reduction: PCA
Feature Subset Selection
Another way to reduce dimensionality of data
Redundant features
duplicate much or all of the information contained in one or more other attributes
Example: purchase price of a product and the amount of sales tax paid
Irrelevant features
contain no information that is useful for the data mining task at hand
Example: students’ ID is often irrelevant to the task of predicting students’ GPA
Feature Subset Selection
Techniques:
Bruteforce approch:
Try all possible feature subsets as input to data mining algorithm
Embedded approaches:
Feature selection occurs naturally as part of the data mining algorithm
Filter approaches:
Features are selected before data mining algorithm is run
Wrapper approaches:
Use the data mining algorithm as a black box to find best subset of attributes
Feature Creation
Create new attributes that can capture the important information in a data set much more efficiently than the original attributes
Three general methodologies:
Feature Extraction
domainspecific
Mapping Data to New Space
Feature Construction
combining features
Mapping Data to a New Space
Two Sine Waves
Two Sine Waves + Noise
Frequency
Fourier transform
Wavelet transform
Discretization Using Class Labels
Entropy based approach
3 categories for both x and y
5 categories for both x and y
Discretization Without Using Class Labels
Data
Equal interval width
Equal frequency
Kmeans
Attribute 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
Simple functions: xk, log(x), ex, x
Standardization and Normalization
Similarity and Dissimilarity
Similarity
Numerical measure of how alike two data objects are.
Is higher when objects are more alike.
Often falls in the range [0,1]
Dissimilarity
Numerical measure of how different are two data objects
Lower when objects are more alike
Minimum dissimilarity is often 0
Upper limit varies
Proximity refers to a similarity or dissimilarity
Similarity/Dissimilarity for Simple Attributes
p and q are the attribute values for two data objects.
Euclidean Distance
Euclidean Distance
Where n is the number of dimensions (attributes) and pk and qk are, respectively, the kth attributes (components) or data objects p and q.
Standardization is necessary, if scales differ.
Euclidean Distance
Distance Matrix
Sheet1
point x y
0 2
p2 2 0
p3 3 1
p4 5 1
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
p1
Sheet2
Sheet3
Sheet1
point x y
0 2
p2 2 0
p3 3 1
p4 5 1
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
p1
Sheet2
Sheet3
Minkowski Distance
Minkowski Distance is a generalization of Euclidean Distance
Where r is a parameter, n is the number of dimensions (attributes) and pk and qk are, respectively, the kth attributes (components) or data objects p and q.
Minkowski Distance: Examples
r = 1. City block (Manhattan, taxicab, L1 norm) distance.
A common example of this is the Hamming distance, which is just the number of bits that are different between two binary vectors
r = 2. Euclidean distance
r . “supremum” (Lmax norm, L norm) distance.
This is the maximum difference between any component of the vectors
Do not confuse r with n, i.e., all these distances are defined for all numbers of dimensions.
Minkowski Distance
Distance Matrix
Sheet1
point x y
0 2
p2 2 0
p3 3 1
p4 5 1
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
L1 p1 p2 p3 p4
p1 0 4 4 6
p2 4 0 2 4
p3 4 2 0 2
p4 6 4 2 0
L2 p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
p1 p2 p3 p4
p1 0 2 3 5
p2 2 0 1 3
p3 3 1 0 2
p4 5 3 2 0
p1
Sheet2
Sheet3
Sheet1
point x y
0 2
p2 2 0
p3 3 1
p4 5 1
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
L1 p1 p2 p3 p4
p1 0 4 4 6
p2 4 0 2 4
p3 4 2 0 2
p4 6 4 2 0
L2 p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
p1 p2 p3 p4
p1 0 2 3 5
p2 2 0 1 3
p3 3 1 0 2
p4 5 3 2 0
p1
Sheet2
Sheet3
Sheet1
point x y
0 2
p2 2 0
p3 3 1
p4 5 1
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
L1 p1 p2 p3 p4
p1 0 4 4 6
p2 4 0 2 4
p3 4 2 0 2
p4 6 4 2 0
L2 p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
p1 p2 p3 p4
p1 0 2 3 5
p2 2 0 1 3
p3 3 1 0 2
p4 5 3 2 0
p1
Sheet2
Sheet3
Sheet1
point x y
0 2
p2 2 0
p3 3 1
p4 5 1
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
point x y
p1 0 2
p2 2 0
p3 3 1
p4 5 1
L1 p1 p2 p3 p4
p1 0 4 4 6
p2 4 0 2 4
p3 4 2 0 2
p4 6 4 2 0
L2 p1 p2 p3 p4
p1 0 2.828 3.162 5.099
p2 2.828 0 1.414 3.162
p3 3.162 1.414 0 2
p4 5.099 3.162 2 0
p1 p2 p3 p4
p1 0 2 3 5
p2 2 0 1 3
p3 3 1 0 2
p4 5 3 2 0
p1
Sheet2
Sheet3
Mahalanobis Distance
For red points, the Euclidean distance is 14.7, Mahalanobis distance is 6.
is the covariance matrix of the input data X
Mahalanobis Distance
Covariance Matrix:
B
A
C
A: (0.5, 0.5)
B: (0, 1)
C: (1.5, 1.5)
Mahal(A,B) = 5
Mahal(A,C) = 4
Common Properties of a Distance
Distances, such as the Euclidean distance, have some well known properties.
d(p, q) 0 for all p and q and d(p, q) = 0 only if
p = q. (Positive definiteness)
d(p, q) = d(q, p) for all p and q. (Symmetry)
d(p, r) d(p, q) + d(q, r) for all points p, q, and r.
(Triangle Inequality)
where d(p, q) is the distance (dissimilarity) between points (data objects), p and q.
A distance that satisfies these properties is a metric
Common Properties of a Similarity
Similarities, also have some well known properties.
s(p, q) = 1 (or maximum similarity) only if p = q.
s(p, q) = s(q, p) for all p and q. (Symmetry)
where s(p, q) is the similarity between points (data objects), p and q.
Similarity Between Binary Vectors
Common situation is that objects, p and q, have only binary attributes
Compute similarities using the following quantities
M01 = the number of attributes where p was 0 and q was 1
M10 = the number of attributes where p was 1 and q was 0
M00 = the number of attributes where p was 0 and q was 0
M11 = the number of attributes where p was 1 and q was 1
Simple Matching and Jaccard Coefficients
SMC = number of matches / number of attributes
= (M11 + M00) / (M01 + M10 + M11 + M00)
J = number of 11 matches / number of notbothzero attributes values
= (M11) / (M01 + M10 + M11)
SMC versus Jaccard: Example
p = 1 0 0 0 0 0 0 0 0 0
q = 0 0 0 0 0 0 1 0 0 1
M01 = 2 (the number of attributes where p was 0 and q was 1)
M10 = 1 (the number of attributes where p was 1 and q was 0)
M00 = 7 (the number of attributes where p was 0 and q was 0)
M11 = 0 (the number of attributes where p was 1 and q was 1)
SMC = (M11 + M00)/(M01 + M10 + M11 + M00) = (0+7) / (2+1+0+7) = 0.7
J = (M11) / (M01 + M10 + M11) = 0 / (2 + 1 + 0) = 0
Cosine Similarity
If d1 and d2 are two document vectors, then
cos( d1, d2 ) = (d1 d2) / d1 d2 ,
where indicates vector dot product and  d  is the length of vector d.
Example:
d1 = 3 2 0 5 0 0 0 2 0 0
d2 = 1 0 0 0 0 0 0 1 0 2
d1 d2= 3*1 + 2*0 + 0*0 + 5*0 + 0*0 + 0*0 + 0*0 + 2*1 + 0*0 + 0*2 = 5
d1 = (3*3+2*2+0*0+5*5+0*0+0*0+0*0+2*2+0*0+0*0)0.5 = (42) 0.5 = 6.481
d2 = (1*1+0*0+0*0+0*0+0*0+0*0+0*0+1*1+0*0+2*2) 0.5 = (6) 0.5 = 2.245
cos( d1, d2 ) = .3150
Extended Jaccard Coefficient (Tanimoto)
Variation of Jaccard for continuous or count attributes
Reduces to Jaccard for binary attributes
Correlation
Correlation measures the linear relationship between objects
To compute correlation, we standardize data objects, p and q, and then take their dot product
Visually Evaluating Correlation
Scatter plots showing the similarity from –1 to 1.
General Approach for Combining Similarities
Sometimes attributes are of many different types, but an overall similarity is needed.
Using Weights to Combine Similarities
May not want to treat all attributes the same.
Use weights wk which are between 0 and 1 and sum to 1.
Density
Densitybased clustering require a notion of density
Examples:
Euclidean density
Euclidean density = number of points per unit volume
Probability density
Graphbased density
Euclidean Density – Cellbased
Simplest approach is to divide region into a number of rectangular cells of equal volume and define density as # of points the cell contains
Euclidean Density – Centerbased
Euclidean density is the number of points within a specified radius of the point
Tid
Refund
Marital
Status
Taxable
Income
Cheat
1
Yes
Single
125K
No
2
No
Married
100K
No
3
No
Single
70K
No
4
Yes
Married
120K
No
5
No
Divorced
95K
Yes
6
No
Married
60K
No
7
Yes
Divorced
220K
No
8
No
Single
85K
Yes
9
No
Married
75K
No
10
No
Singl
e
90K
Yes
10
1
2
3
5
5
7
8
15
10
4
A
B
C
D
E
1.1
2.2
16.22
6.25
12.65
1.2
2.7
15.22
5.27
10.23
Thickness
Load
Distance
Projection
of y load
Projection
of x Load
1.1
2.2
16.22
6.25
12.65
1.2
2.7
15.22
5.27
10.23
Thickness
Load
Distance
Projection
of y load
Projection
of x Load
Document 1
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Document 2
Document 3
3050260202
0
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702100300
100122030
TID
Items
1
Bread, Coke, Milk
2
Beer, Bread
3
Beer, Coke, Diaper, Milk
4
Beer, Bread, Diaper, Milk
5
Coke, Diaper, Milk
5
2
1
2
5
Data Mining
Graph Partitioning
Parallel Solution of Sparse Linear System of Equations
NBody Computation and Dense Linear System Solvers
GGTTCCGCCTTCAGCCCCGCGCC
CGCAGGGCCCGCCCCGCGCCGTC
GAGAAGGGCCCGCCTGGCGGGCG
GGGGGAGGCGGGGCCGCCCGAGC
CCAACCGAGTCCGACCAGGTGCC
CCCTCTGCTCGGCCTAGACCTGA
GCTCATTAGGCGGCAGCGGACAG
GCCAAGTAGAACACGCGAAGCGC
TGGGCTGCCTGCTGCGACCAGGG
Dimensions = 10
Dimensions = 40
Dimensions = 80
Dimensions = 120
Dimensions = 160
Dimensions = 206
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p1
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pointxy
p102
p220
p331
p451
p1p2p3p4
p102.8283.1625.099
p22.82801.4143.162
p33.1621.41402
p45.0993.16220
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