Olap operations with examples ppt

Data Mining: Exploring Data

Lecture Notes for Chapter 3

Introduction to Data Mining

by

Tan, Steinbach, Kumar

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
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.

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)
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):
Setosa
Virginica
Versicolour
Four (non-class) attributes
Sepal width and length
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.

Summary Statistics

Summary statistics are numbers that summarize properties of the data
Summarized properties include frequency, location and spread
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
Percentiles

For continuous data, the notion of a percentile is more useful.
Given an ordinal or continuous attribute x and a number p between 0 and 100, the pth percentile is a value of x such that p% of the observed values of x are less than .

For instance, the 50th percentile is the value such that 50% of all values of x are less than .
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.
image1.png

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
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.
Arrangement

Is the placement of visual elements within a display
Can make a large difference in how easy it is to understand the data
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
See example on the next slide
Visualization Techniques: Contour Plots

Contour plots
Useful when a continuous attribute is measured on a spatial grid
They partition the plane into regions of similar values
The contour lines that form the boundaries of these regions connect points with equal values
The most common example is contour maps of elevation
Can also display temperature, rainfall, air pressure, etc.
An example for Sea Surface Temperature (SST) is provided on the next slide
Visualization Techniques: Parallel Coordinates

Parallel Coordinates
Used to plot the attribute values of high-dimensional data
Instead of using perpendicular axes, use a set of parallel axes
The attribute values of each object are plotted as a point on each corresponding coordinate axis and the points are connected by a line
Thus, each object is represented as a line
Often, the lines representing a distinct class of objects group together, at least for some attributes
Ordering of attributes is important in seeing such groupings
Other Visualization Techniques

Star Plots
Similar approach to parallel coordinates, but axes radiate from a central point
The line connecting the values of an object is a polygon
Chernoff Faces
Approach created by Herman Chernoff
This approach associates each attribute with a characteristic of a face
The values of each attribute determine the appearance of the corresponding facial characteristic
Each object becomes a separate face
Relies on human’s ability to distinguish faces
OLAP

On-Line Analytical Processing (OLAP) was proposed by E. F. Codd, the father of the relational database.
Relational databases put data into tables, while OLAP uses a multidimensional array representation.
Such representations of data previously existed in statistics and other fields
There are a number of data analysis and data exploration operations that are easier with such a data representation.
Creating a Multidimensional Array

Two key steps in converting tabular data into a multidimensional array.
First, identify which attributes are to be the dimensions and which attribute is to be the target attribute whose values appear as entries in the multidimensional array.
The attributes used as dimensions must have discrete values
The target value is typically a count or continuous value, e.g., the cost of an item
Can have no target variable at all except the count of objects that have the same set of attribute values
Second, find the value of each entry in the multidimensional array by summing the values (of the target attribute) or count of all objects that have the attribute values corresponding to that entry.
OLAP Operations: Data Cube

The key operation of a OLAP is the formation of a data cube
A data cube is a multidimensional representation of data, together with all possible aggregates.
By all possible aggregates, we mean the aggregates that result by selecting a proper subset of the dimensions and summing over all remaining dimensions.
For example, if we choose the species type dimension of the Iris data and sum over all other dimensions, the result will be a one-dimensional entry with three entries, each of which gives the number of flowers of each type.
Consider a data set that records the sales of products at a number of company stores at various dates.
This data can be represented
as a 3 dimensional array
There are 3 two-dimensional
aggregates (3 choose 2 ),
3 one-dimensional aggregates,
and 1 zero-dimensional
aggregate (the overall total)
Data Cube Example

The following figure table shows one of the two dimensional aggregates, along with two of the one-dimensional aggregates, and the overall total
Data Cube Example (continued)

OLAP Operations: Slicing and Dicing

Slicing is selecting a group of cells from the entire multidimensional array by specifying a specific value for one or more dimensions.
Dicing involves selecting a subset of cells by specifying a range of attribute values.
This is equivalent to defining a subarray from the complete array.
In practice, both operations can also be accompanied by aggregation over some dimensions.
OLAP Operations: Roll-up and Drill-down

Attribute values often have a hierarchical structure.
Each date is associated with a year, month, and week.
A location is associated with a continent, country, state (province, etc.), and city.
Products can be divided into various categories, such as clothing, electronics, and furniture.
Note that these categories often nest and form a tree or lattice
A year contains months which contains day
A country contains a state which contains a city
OLAP Operations: Roll-up and Drill-down

This hierarchical structure gives rise to the roll-up and drill-down operations.
For sales data, we can aggregate (roll up) the sales across all the dates in a month.
Conversely, given a view of the data where the time dimension is broken into months, we could split the monthly sales totals (drill down) into daily sales totals.
Likewise, we can drill down or roll up on the location or product ID attributes.