Transcript Evaluation of MineSet 3.0

Classifier
Decision Tree
•A decision tree classifies data by predicting the label
for each record.
•The first element of the tree is the root node,
representing all of the data.
•From there, the tree spits into two or more
branches, each representing data with different
values for a specific attribute (column).
•The object is to reach nodes at the ends of the
branches (leaf nodes) where the records all, or nearly
all, have the same class (label).
Classifier
Option Tree
•Like Decision Tree classifiers, Option Tree
classifiers also assign each record to a class.
•An option tree actually consists of several decision
trees.
•Instead of picking an attribute to split on for the
root node, it picks several, and makes a decision tree
for each.
Option Tree
Evidence
•It incorporates an inducer (an algorithm for
generating Naïve Bayes models) and a visualizer.
•Unlike the Decision Table model, the Evidence
model assumes that the attributes are independent.
•Once the classifier is built, the results are displayed
in the Evidence Visualizer window.
Evidence
Decision Table
•A decision table is a predictive modeling tool that
performs classification for more information on
classifiers and predictive modeling.
•A decision table is a hierarchical breakdown of the
data, with two attributes at each level of the
hierarchy.
•The Decision Table inducer identifies the most
important attributes (columns) for classifying the
data, and the accompanying visualizer displays the
resulting model graphically as a series of cake charts
Decision Table
Regression
•Regression is the task of predicting a continuous
label value, given a set of descriptive attributes.
• When a regressor is generated, the Option Tree
Visualizer displays the tree.
• In addition, it can provide valuable insight into the
data itself.
VISUALIZATION TOOL
• 2D Visualizers
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3D Visualizers
 Record.
Scatter
 Statistics.
Splat
 Box Plots
Tree
 Histogram.
Map
Record Visualizer
An easy way to become familiar with your
selected dataset is to use the Record Viewer to see the
database records and the data values within the
columns. The records appear in spreadsheet form.
Record Visualizer
Record Visualizer
Supported Operations :
• Resize columns
• Rearrange columns
• Hide Columns
• Sort record order by column values
• Sort record order by multiple columns
• Reverse the sort
Statistics Visualizer
• One can find out more about the records
in the dataset with the Statistics Visualizer
•Certain statistics are calculated, based on
the number of records in the dataset.
•Depending on whether the type of data in
the column is numeric or discrete, statistics
are shown as box plots and histograms,
respectively.
Statistics Visualizer
Box Plots
•The box plots show
• Minimum
• Maximum
• Mean
• Median
• Two quartiles (25th and 75th percentiles) of numeric
values in a column, as lines across a vertical colored bar
• The standard deviation of the dataset population is
shown as a +/- value.
Statistics Visualizer
Histogram visualizer
•The histograms show results from columns of non-numeric
data, such as string, or bin.
•The columns in the data table can contain strings such as
“yes” or “spore_color,” or binned values such as “10-70.”
•There can be up to 100 distinct entries like the above.
•The default ordering of the string-valued (nominal)
attributes is by decreasing count, but you can use the View
pull down menu to choose an alternative sorting.
Histogram visualizer
Scatter Visualizer
•The Scatter Visualizes individual datapoints
correspond to rows in the data file.
•This visualization works well when the number of
datapoints is less than 50,000, or when some
processing has been performed so that the data is
reduced to a small set of aggregates.
•The Scatter Visualizer produces scatter plots that
can be animated to show relationships more clearly.
Scatter Visualizer
Scatter Visualizer
•The Scatter Visualizer displays a three-dimensional
landscape with columns of data mapped to entities,
and to elements such as the axes, size and color.
•If you map one or two numeric variables to the
sliders, you can animate the size, color, or position of
the entities.
Splat Visualizer
•With the Splat Visualizer you can visually analyze
relationships among several variables .
•With some relationships seen even more clearly when you
use the animation feature.
•The Splat Visualizer uses graphical objects, called splats,
which represent aggregates of data points.
•The color and opacity, but not the position, of the splats can
change during animation.
Splat Visualizer
Tree Visulaizer
•The Tree Visualizer is a graphical interface that displays
data as a three-dimensional landscape.
•Each level of the tree branches on the values of a different
attribute.
• Each node in the tree shows a chart representing all the
data in the subtree below it.
•The chart is composed of a base block with height and color
depending on the data attributes.
• On each base are bars and/or disks whose number, label,
height, and color are also determined by the attributes you
have specified.
Tree Visulaizer
Map Visualizer
•The Map Visualizer helps you look at spatially related data.
•Besides dynamically navigating through this geographically
based landscape, you can drill up and down to get an
overview or to see increased granularity.
•Use animation to see how the data changes across one or
two independent dimensions.
Map Visualizer
Functions Supported by
Visualizers
Animations
Automatic Slider Creation
Manual Slider Creation
Animation Trails
No trails /Line trails/ Fade-out trails /Tube trails
Animations
Manipulating Scatter and Splat
Visualizer Results
•Changing the Displays
Panel, Set Background Color, Window Decoration,
Animation Panel animation.
•Drilling
•Changing the Scatter Visualizer Display with the
Shape Menu
Cube
Diamond
Sphere
Bar
Manipulating Scatter and Splat
Visualizer Results
Changing the Splat Visualizer Display with the
Shape Menu
•Linear
•Gaussian
•Texture
Navigation Support in Tree
Visualizer
Navigation Support in a nontree visualizer
HISTORY OF OPERATIONS
VIEW
•This tab displays all the changes done to the dataset
from the time of opening it.
•The operations done so far are shown in the form of
a graph, which is present on the left part of the
window.
•Clicking the node that represents the operation.
Scalability
•MineSet is optimized for the ultimate scalable
performance on the Origin server.
• MineSet offers very large memory support (over
2GB) and data handling capability using the 64-bit
implementation.
•NT platform. Parallelization and 64-bit support are
offered on the highly scalable Silicon Graphics
Origin servers.
IN CLOSING
MineSet scores good in the foillowing:
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Multi Data Access.
Multi Platform Ability.
Scalability.
Visualization.
Mining Tools.
Mining Algorithms.
Data Transformation Functions.
Data Manipulation in Displays.
It’s Simplicity as a whole.
I18N Support for Multibyte International
IN CLOSING
Limitations
•Multi Source Integration.
•Density based Clustering.
•Mining Multimedia Databases.
•Mining text data.
•Mining the World Wide Web.
Source
http://www.sgi.com/software/mineset/.