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

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

Chapter 3 Exploring Data

1st Step of Machine Learning

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

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.

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

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)

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

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

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

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

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

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.

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

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.

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

Visualization

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

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

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

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

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.

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

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

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

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:

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

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

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

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)

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

Two-Dimensional Histograms

! Show the joint distribution of the values of two attributes

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

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

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

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

Example of Box Plots

! Box plots can be used to compare attributes

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

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

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

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.

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

Scatter Plot Array of Iris Attributes

We are a professional custom writing website. If you have searched a question and bumped into our website just know you are in the right place to get help in your coursework.

Yes. We have posted over our previous orders to display our experience. Since we have done this question before, we can also do it for you. To make sure we do it perfectly, please fill our Order Form. Filling the order form correctly will assist our team in referencing, specifications and future communication.

1. Click on the “**Place order** tab at the top menu or “**Order Now**” icon at the
bottom and a new page will appear with an order form to be filled.

2. Fill in your paper’s requirements in the "**PAPER INFORMATION**" section
and click “**PRICE CALCULATION**” at the bottom to calculate your order
price.

3. Fill in your paper’s academic level, deadline and the required number of pages from the drop-down menus.

4. Click “**FINAL STEP**” to enter your registration details and get an account
with us for record keeping and then, click on “**PROCEED TO CHECKOUT**”
at the bottom of the page.

5. From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.

Need this assignment or any other paper?

Click here and claim 25% off

Discount code SAVE25