Discussion – research and answer

After completing the reading this week answer the following questions:

Chapter 2:

What is an attribute and note the importance?
What are the different types of attributes?
What is the difference between discrete and continuous data?
Why is data quality important?
What occurs in data preprocessing?
In section 2.4, review the measures of similarity and dissimilarity, select one topic and note the key factors.

©Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#›

Dr. Oner Celepcikay

ITS 632

Data Mining

Summer 2019Week 2: Data & Data Exploration

Chapter 3 Exploring Data

1st Step of Machine Learning

What is data exploration?

! Key motivations of data exploration include
– Helping to select the right tool for preprocessing or analysis
– Making use of humans’ abilities to recognize patterns

u People can recognize patterns not captured by data analysis
tools

! Related to the area of Exploratory Data Analysis (EDA)
– Created by statistician John Tukey
– Seminal book is Exploratory Data Analysis by Tukey
– A nice online introduction can be found in Chapter 1 of the NIST

Engineering Statistics Handbook
http://www.itl.nist.gov/div898/handbook/index.htm

A preliminary exploration of the data to
better understand its characteristics.

http://www.itl.nist.gov/div898/handbook/index.htm

Techniques Used In Data Exploration

! In EDA, as originally defined by Tukey
– The focus was on visualization
– Clustering and anomaly detection were viewed as

exploratory techniques
– In data mining, clustering and anomaly detection are

major

areas

of interest, and not thought of as just
exploratory

! In our discussion of data exploration, we focus on
– Summary statistics
–

Visualization

– Online Analytical Processing (OLAP)

Summary Statistics

! Summary statistics are numbers that summarize
properties of the data

– Summarized properties include frequency, location and
spread
u Examples: location – mean

spread – standard deviation

– Most summary statistics can be calculated in a single
pass through the data

Frequency and Mode

!The frequency of an attribute value is the
percentage of time the value occurs in the
data set
– For example, given the attribute ‘gender’ and a

representative population of people, the gender
‘female’ occurs about 50% of the time.

! The mode of a an attribute is the most frequent
attribute value

! The notions of frequency and mode are typically
used with categorical data

Measures of Location: Mean and Median

! The mean is the most common measure of the
location of a set of points.

! However, the mean is very sensitive to outliers.
! Thus, the median or a trimmed mean is also

commonly used.

Measures of Spread: Range and Variance

! Range is the difference between the max and min
! The variance or standard deviation is the most

common measure of the spread of a set of points.

! However, this is also sensitive to outliers, so that
other measures are often used.

Visualization is the conversion of data into a visual
or tabular format so that the characteristics of the
data and the relationships among data items or
attributes can be analyzed or reported.

! Visualization of data is one of the most powerful
and appealing techniques for data exploration.
– Humans have a well developed ability to analyze large

amounts of information that is presented visually
– Can detect general patterns and trends
– Can detect outliers and unusual patterns

Example: Sea Surface Temperature

! The following shows the Sea Surface
Temperature (SST) for July 1982
– Tens of thousands of data points are summarized in a

single figure

Representation

! Is the mapping of information to a visual format
! Data objects, their attributes, and the relationships

among data objects are translated into graphical
elements such as points, lines, shapes, and
colors.

! Example:
– Objects are often represented as points
– Their attribute values can be represented as the

position of the points or the characteristics of the
points, e.g., color, size, and shape

– If position is used, then the relationships of points, i.e.,
whether they form groups or a point is an outlier, is
easily perceived.

One Great Example

! The Power of Visualization by Hans Rosling

https://www.ted.com/talks/hans_rosling_shows_the_best
_stats_you_ve_ever_seen?language=en

Arrangement

! Is the placement of visual elements within a
display

! Can make a large difference in how easy it is to
understand the data

! Example:

Selection

! Is the elimination or the de-emphasis of certain
objects and attributes

! Selection may involve the chossing a subset of
attributes
– Dimensionality reduction is often used to reduce the

number of dimensions to two or three
– Alternatively, pairs of attributes can be considered

! Selection may also involve choosing a subset of
objects
– A region of the screen can only show so many points
– Can sample, but want to preserve points in sparse

areas

Visualization Techniques: Histograms

! Histogram
– Usually shows the distribution of values of a single variable
– Divide the values into bins and show a bar plot of the number of

objects in each bin.
– The height of each bar indicates the number of objects
– Shape of histogram depends on the number of bins

! Example: Petal Width (10 and 20 bins, respectively)

Two-Dimensional Histograms

! Show the joint distribution of the values of two
attributes

! Example: petal width and petal length
– What does this tell us?

Visualization Techniques: Box Plots

! Box Plots
– Invented by J. Tukey
– Another way of displaying the distribution of data
– Following figure shows the basic part of a box plot

outlier

10th percentile

25th percentile

75th percentile

50th percentile

10th percentile

Example of Box Plots

! Box plots can be used to compare attributes

Visualization Techniques: Scatter Plots

! Scatter plots
– Attributes values determine the position
– Two-dimensional scatter plots most common, but can

have three-dimensional scatter plots
– Often additional attributes can be displayed by using

the size, shape, and color of the markers that
represent the objects

– It is useful to have arrays of scatter plots can
compactly summarize the relationships of several pairs
of attributes
u See example on the next slide

Iris Sample Data Set

! Many of the exploratory data techniques are illustrated
with the Iris Plant data set.

– Can be obtained from the UCI Machine Learning Repository
http://www.ics.uci.edu/~mlearn/MLRepository.html

– From the statistician Douglas Fisher
– Three flower types (classes):

u Setosa
u Virginica
u Versicolour

– Four (non-class) attributes
u Sepal width and length
u Petal width and length Virginica. Robert H. Mohlenbrock. USDA

NRCS. 1995. Northeast wetland flora: Field
office guide to plant species. Northeast National
Technical Center, Chester, PA. Courtesy of
USDA NRCS Wetland Science Institute.

http://www.ics.uci.edu/~mlearn/MLRepository.html

Scatter Plot Array of Iris Attributes

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