DMTM 2015 - 16 Data Preparation

Prof. Pier Luca Lanzi
Data Preparation
Data Mining andText Mining (UIC 583 @ Politecnico di Milano)

Why Data Preprocessing?
•  Data in the real world are “dirty”
•  They are usually incomplete
§ Missing attribute values
§ Missing attributes of interest, or containing only aggregate data
•  They are usually noisy, containing errors or outliers
§ e.g., Salary=“-10”
•  They are usually inconsistent and contain discrepancies
§ 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
2

Why Is Data Dirty?
•  Incomplete 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
•  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
§ Different data sources
§ Functional dependency violation (e.g., modify some linked data)
•  Duplicate records also need data cleaning
3

Why Is Data Preprocessing Important?
•  No quality in data, no quality in the mining results!
(trash in, trash out)
•  Quality decisions needs quality in data
•  E.g., duplicate or missing data may cause incorrect or even
misleading statistics.
•  Data warehouses need consistent integration of quality data
•  Data extraction, cleaning, and transformation comprises the
majority of the work of building a data warehouse
4

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
5

Outliers
•  Outliers are data objects with characteristics that are considerably
different than most of the other data objects in the data set
6

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)
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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
8

Data Preprocessing
•  Aggregation
•  Sampling
•  Dimensionality Reduction
•  Feature subset selection
•  Feature creation
•  Discretization and Binarization
9

Aggregation
•  Combining two or more attributes (or objects)
into a single attribute (or object)
•  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
10

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
•  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
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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
12

2000 Points8000 points 500 Points

Sample Size
•  What sample size is necessary to get at least
one object from each of 10 groups.
14

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 non-linear techniques
15

Dimensionality Reduction:
Principal Component Analysis (PCA)
•  Given N data vectors from n-dimensions, ﬁnd k ≤ n orthogonal
vectors (principal components) that can be best used to
represent data
•  Works for numeric data only
•  Used when the number of dimensions is large
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•  Normalize input data
•  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
17

•  Goal is to ﬁnd a projection that captures the largest amount of
variation in data
18
Original axes
**
*
*
*
*
* *
*
*
*
*
*
*
*
*
*
* *
*
*
*
*
*
Data points
First principal componentSecond principal component

Singular Value Decomposition
•  Theorem (Press 1992). It is always possible to decompose matrix
A into A = UΛVT where U, Λ, V are unique
•  U, V are column orthonormal, i.e., columns are unit vectors,
orthogonal to each other, so that UTU = I; VTV = I
•  Li,i are the singular values, they are positive, and sorted in
decreasing order
21

•  Data are viewed as a matrix A with n examples described by m
numerical attributes

A[n x m] = U[n x r] Λ [ r x r] (V[m x r])T
•  A stores the original data
•  U represents the n examples using r new concepts/attributes
•  Λ represents the strength of each ‘concept’ (r is the rank of A)
•  V contain m terms, r concepts
•  Λi,i are called the singular values of A, if A is singular, some of the
wi will be 0; in general rank(A) = number of nonzero wi
•  SVD is mostly unique (up to permutation of singular values, or if
some Li are equal)
22

for Dimensionality Reduction
•  Suppose you want to ﬁnd best rank-k approximation to A
•  Set all but the largest k singular values to zero
•  Can form compact representation by eliminating columns of U
and V corresponding to zeroed Li
23
1 1 1 0 0
2 2 2 0 0
1 1 1 0 0
5 5 5 0 0
0 0 0 2 2
0 0 0 3 3
0 0 0 1 1
0.18 0
0.36 0
0.18 0
0.90 0
0 0.53
0 0.80
0 0.27
=
9.64 0
0 5.29
x
0.58 0.58 0.58 0 0
0 0 0 0.71 0.71
x

1 1 1 0 0
2 2 2 0 0
1 1 1 0 0
5 5 5 0 0
0 0 0 2 2
0 0 0 3 3
0 0 0 1 1
0.18
0.36
0.18
0.90
0
0
0
~
9.64
x
0.58 0.58 0.58 0 0
x
24

1 1 1 0 0
2 2 2 0 0
1 1 1 0 0
5 5 5 0 0
0 0 0 2 2
0 0 0 3 3
0 0 0 1 1
~
1 1 1 0 0
2 2 2 0 0
1 1 1 0 0
5 5 5 0 0
0 0 0 0 0
0 0 0 0 0
0 0 0 0 0
25

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
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Feature Subset Selection
•  Brute-force approach
§ 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 using a procedure that is
independent from a specific data mining algorithm
§ E.g., attributes are selected based on correlation measures
•  Wrapper approaches
§ Use a data mining algorithm as a black box
to find best subset of attributes
§ E.g., apply a genetic algorithm and an algorithm for decision tree to
find the best set of features for a decision tree
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Example of using the information gain to rank the importance of attributes.

Decision trees applied to all the attributes.

Decision trees applied to all the breast dataset without the four least ranked attributes.

Wrapper approach using exhaustive search and decision trees.

Feature Creation
•  Create new attributes that can capture the important information
in a data set much more efﬁciently than the original attributes
•  E.g., given the birthday, create the attribute age
•  Three general methodologies:
§ Feature Extraction: domain-speciﬁc
§ Mapping Data to New Space
§ Feature Construction: combining features
32

Discretization
•  Three types of attributes
§ Nominal: values from an unordered set, e.g., color
§ Ordinal: values from an ordered set,
e.g., military or academic rank
§ Continuous: real numbers,
e.g., integer or real numbers
•  Discretization
§ Divide the range of a continuous attribute into intervals
§ Some classiﬁcation algorithms only accept categorical
attributes
§ Reduce data size by discretization
§ Prepare for further analysis
33

Discretization Approaches
•  Supervised
§ Attributes are discretized using the class information
§ Generates intervals that tries to minimize the loss of
information about the class
•  Unsupervised
§ Attributes are discretized solely based on their values
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Supervised Discretization
•  Entropy based approach
35
3 categories for both x and y 5 categories for both x and y

Discretization Without Using Class Labels 36
Data Equal interval width
Equal frequency K-means

Segmentation by Natural Partitioning
•  A simply 3-4-5 rule can be used to segment numeric data into
relatively uniform, “natural” intervals.
•  If an interval covers 3, 6, 7 or 9 distinct values at the most
significant digit, partition the range into 3 equi-width intervals (3
equal-width intervals for 3, 6, and 9; and 3 intervals in the
grouping of 2-3-2 for 7)
•  If it covers 2, 4, or 8 distinct values at the most significant digit,
partition the range into 4 intervals
•  If it covers 5 or 10 distinct values at the most significant digit,
partition the range into 5 intervals
37

Summary
•  Data exploration and preparation, or preprocessing, is a big issue
for both data warehousing and data mining
•  Descriptive data summarization is need for quality data
preprocessing
•  Data preparation includes
§ Data cleaning and data integration
§ Data reduction and feature selection
§ Discretization
•  A lot a methods have been developed but data preprocessing still
an active area of research
38

DMTM 2015 - 16 Data Preparation

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DMTM 2015 - 16 Data Preparation