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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1
Data Mining: Data
Lecture Notes for Chapter 2
Introduction to Data Miningby
Tan, Steinbach, Kumar
Revised by QY
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2
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
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
Attributes
Objects
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3
Attribute Values
Attribute values are numbers or symbols assigned to an attribute– E.g. ‘Student Name’=‘John’– Attributes are also called ‘variables’, or ‘features’– Attribute values are also called ‘values’, or ‘feature-
values’ Designing Attributes for a data set requires
domain knowledge– Always have an objective in mind (e.g., what is the
class attribute?)– Design a ‘movie’ data set for a movie dataset?
What is domain knowledge?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4
Measurement of Length
Different designs have different attributes properties.
1
2
3
5
5
7
8
15
10 4
A
B
C
D
E
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 5
Types of Attributes
There are different types of attributes– Nominal (Categorical)
Examples: ID numbers, eye color, zip codes
– Ordinal (Categorical) Examples: rankings (e.g., movie ranking scores on a scale
from 1-10), grades (A,B,C..), height in {tall, medium, short}
– Binary (0, 1) is a special case
– Continuous Example: temperature in Celsius
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 6
Record Data
Data consist 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
Q: what is a sparse data set?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 7
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 multi-dimensional space, where each dimension represents an 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
1.12.216.226.2512.65
1.22.715.225.2710.23
Thickness LoadDistanceProjection of y load
Projection of x Load
1.12.216.226.2512.65
1.22.715.225.2710.23
Thickness LoadDistanceProjection of y load
Projection of x Load
Q: what is a sparse data set?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 8
Document Data
Each document becomes a `term' vector, – each term is a component (attribute) of the vector,
Term can be n-grams, phrases, etc.
– 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
Q: what is a sparse data set?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 9
Transaction Data
A special type of record data, where – each record (transaction) has 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.
– Set based TID Items
1 Bread, Coke, Milk
2 Beer, Bread
3 Beer, Coke, Diaper, Milk
4 Beer, Bread, Diaper, Milk
5 Coke, Diaper, Milk
Q: class attribute?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 10
Graph Data
Examples: Directed graph and URL Links
5
2
1
2
5
<a href="papers/papers.html#bbbb">Data Mining </a><li><a href="papers/papers.html#aaaa">Graph Partitioning </a><li><a href="papers/papers.html#aaaa">Parallel Solution of Sparse Linear System of Equations </a><li><a href="papers/papers.html#ffff">N-Body Computation and Dense Linear System Solvers
Q: what is a sparse data set?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 11
Ordered Data
Sequences of transactions
An element of the sequence
Items/Events
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 12
Ordered Data
Genomic sequence data
GGTTCCGCCTTCAGCCCCGCGCCCGCAGGGCCCGCCCCGCGCCGTCGAGAAGGGCCCGCCTGGCGGGCGGGGGGAGGCGGGGCCGCCCGAGCCCAACCGAGTCCGACCAGGTGCCCCCTCTGCTCGGCCTAGACCTGAGCTCATTAGGCGGCAGCGGACAGGCCAAGTAGAACACGCGAAGCGCTGGGCTGCCTGCTGCGACCAGGG
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 13
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
– duplicated data
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 14
Outliers
Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set– Are they noise points, or meaningful outliers?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 15
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)
– Missing as meaningful…
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 16
Data Preprocessing
Aggregation and Noise Removal Sampling Dimensionality Reduction Feature subset selection Feature creation and transformation Discretization
Q: How much % of the data mining process is data preprocessing?
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 17
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
– De-noise: more “stable” data Aggregated data tends to have less variability
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 18
Aggregation
Standard Deviation of Average Monthly Precipitation
Standard Deviation of Average Yearly Precipitation
Variation of Precipitation in Australia
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 19
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.
Reasons:– too expensive or time consuming to obtain or to process
the data.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 20
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
Thus, harder and harder to classify the data!
• Randomly generate 500 points
• Compute difference between max and min distance between any pair of points
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 21
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 (supervised and unsupervised methods)– Principle Component Analysis
– Singular Value Decomposition
– Others: supervised and non-linear techniques
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 22
Dimensionality Reduction: PCA
Goal is to find a projection that captures the largest amount of variation in data– Supervised or unsupervised?
x2
x1
e
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 23
Dimensionality Reduction: PCA
Find the eigenvectors of the covariance matrix The eigenvectors define the new space
– How many eigenvectors here?
x2
x1
e
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 24
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)
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 25
Dimensions = 10Dimensions = 40Dimensions = 80Dimensions = 120Dimensions = 160Dimensions = 206
Dimensionality Reduction: PCA
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 26
Question
What is the difference between sampling and dimensionality reduction?– Thining vs. shortening of data
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 27
Discretization
Three types of attributes:– Nominal — values from an unordered set
Example: attribute “outlook” from weather data
– Values: “sunny”,”overcast”, and “rainy”– Ordinal — values from an ordered set
Example: attribute “temperature” in weather data
– Values: “hot” > “mild” > “cool”– Continuous — real numbers
Discretization: – divide the range of a continuous attribute into intervals– Some classification algorithms only accept categorical attributes.– Reduce data size by discretization– Supervised (entropy) vs. Unsupervised (binning)
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 28
Simple Discretization Methods: Binning
Equal-width (distance) partitioning:– It 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 straightforwardBut outliers may dominate presentation: Skewed data is not handled well.
Equal-depth (frequency) partitioning:– It divides the range into N intervals, each containing
approximately same number of samples– Good data scaling– Managing categorical attributes can be tricky.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 29
Transforming Ordinal to Boolean
Simple transformation allows to code ordinal attribute with n values using n-1 boolean attributes
Example: attribute “temperature”
Why? Not introducing distance concept between different colors: “Red” vs. “Blue” vs. “Green”.
Temperature
Cold
Medium
Hot
Temperature > cold Temperature > medium
False False
True False
True True
Original data Transformed data
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 30
Visually Evaluating Correlation
Scatter plots showing the similarity from –1 to 1.
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