2. visualization in data mining

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Motivation
Visualization for Data Mining
• Huge amounts of information
• Limited display capacity of output devices
Visual Data Mining (VDM) is a new approach for
exploring very large data sets, combining traditional
mining methods and information visualization techniques.

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Levels of VDM
 No or very limited integration
 Corresponds to the application of either traditional information
visualization or automated data mining methods.
 Loose integration
 Visualization and automated mining methods are applied
sequentially.
 The result of one step can be used as input for another step.
 Full integration
 Automated mining and visualization methods applied in parallel.
 Combination of the results.

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Methods of Data Visualization
Different methods are available for visualization of data
based on type of data
Data can be
 Univariate
 Bivariate
 Multivariate

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Univariate data
 Measurement of single quantitative variable
 Characterize distribution
 Represented using following methods
 Histogram
 Pie Chart

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Bivariate Data
 Constitutes of paired samples of two quantitative
variables
 Variables are related
 Scatter plots
 Line graphs

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Multivariate Data
 Multi dimensional representation of multivariate
data
 Icon based methods
 Pixel based methods
 Dynamic parallel coordinate system

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Pixel Based Methods
 Approach:
 Each attribute value is represented by one colored pixel
(the value ranges of the attributes are mapped to a
fixed color map).
 The values of each attribute are presented in separate
sub windows.
 Examples:
 Dense Pixel Displays

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Dense Pixel Display
Approach:
 Each attribute value is represented by one colored
pixel (the value ranges of the attributes are mapped
to a fixed color map).
 Different attributes are presented in separate sub
windows.

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Visual Data Mining: Framework and
Algorithm Development
Ganesh, M., Han, E.H., Kumar, V., Shekar, S., &
Srivastava, J. (1996).
Working Paper. Twin Cities, MN: University of Minnesota,
Twin Cities Campus.

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References
 http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:
zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96vis
ual.pdf
 http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in
%20DM-IEEE%20Trans%20Vis.pdf
 http://www.geocities.com/anand_palm/

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Abstract
 VDM refers to refers to the use of visualization techniques in Data
Mining process to
 Evaluate
 Monitor
 Guide
 This paper provides a framework for VDM via the loose coupling of
databases and visualization systems.
 The paper applies VDM towards designing new algorithms that can
learn decision trees by manually refining some of the decisions made
by well known algorithms such as C4.5.

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Components of VQLBCI
 The three major components of VQLBCI are
Visual Representations, Computations and
Events.

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Visual Development of Algorithms
 Most interesting use of visual data mining is the
development of new insights and algorithms.
 The figure below shows the ER diagram for
learning classification decision trees.
 This model allows the user to monitor the quality
and impact of decisions made by the learning
procedure.
 Learning procedure can be refined interactively
via a visual interface.

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ER diagram for the search space of decision tree
learning algorithm

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General Framework
 Learning a classification decision tree from a training data
set can be regarded as a process of searching for the best
decision tree that meets user-provided goal constraints.
 The problem space of this search process consists of
Model Candidates, Model Candidate Generator and Model
Constraints.
 Many existing classification-learning algorithms like C4.5
and CDP fit nicely within this search framework. New
learning algorithms that fit user’s requirements can be
developed by defining the components of the problem
space.

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General Framework
 Model Candidate corresponds to the partial
classification decision tree. Each node of the
decision tree is a Model Atom
 Search process is the process of finding a final
model candidate such that it meets user goal
specifications.
 Model Candidate Generator transforms the
current model candidate into a new model
candidate by selecting one model atom to expand
from the expandable leaf model atoms.
 Model Constraints (used by Model Candidate
Generator) provide controls and boundaries to the
search space.

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Acceptability Constraint
 Model Constraints consist of Acceptability constraints,
Expandability constraints and a Data-Entropy calculation
function.
 Acceptability constraint predicate specifies when a model
candidate is acceptable and thus allows search process to
stop. EX:
 A1) Total no of expandable leaf model atoms = 0.
 A2) Overall error rate of the model candidate <= acceptable error
rate.
 A3) Total number of model atoms in the model candidate>=
maximal allowable tree size.
A1 is used in C4.5 and CDP

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Expandability Constraint
 An Expandability constraint predicate specifies
whether a leaf model atom is expandable or not.
EX:
 C4.5 uses E1 and E2
 CDP uses E2 and E3

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Traversal Strategy
 Traversal strategy ranks expandable leaf model
atoms based on the model atom attributes. EX:
 Increasing order of depth
 Decreasing order of depth
 Orders based on other model atom attributes.

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Steps in Visual Algorithm Development
 No single algorithm is the best all the time,
performance is highly data dependent.
 By changing different predicates of model
constraints, users can construct new
classification-learning algorithm.
 This enables users to find an algorithm that works
the best on a given data set.
 Two algorithms are developed : BF based on
Best First search idea and CDP+ which is a
modification of CDP

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BF
 This algorithm is based on the Best-First search
idea.
 For Acceptability criteria, it includes A1 and A2
with a user specified acceptable error rate.
 The Traversal strategy chosen is T3
 In Best-First, expandable leaf model atoms are
ranked according to the decreasing order of the
number of misclassified training cases. (local error
rate * size of subset training data set)
 The traversal strategy will expand a model atom
that has the most misclassified training cases,
thus reducing the overall error rate the most.

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CDP +
 CDP+ is a modification of CDP
 CDP has dynamic pruning using expandability
constraint E3.
 Here, the depth is modified according to the size
of the training data set of the model atom.
 We set
 B is the branching factor of the decision tree, t is
the size of training data set belonging to model
atom, T is the whole training data set.

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Comparison of different classification learning
algorithms

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Experiment
 The new BF and CDP+ algorithms are compared
with the C4.5 and CDP algorithms.
 Various metrics are selected to compare the
efficiency, accuracy and size of final decision
trees of the classification algorithm.
 The generation efficiency of the nodes is
measured in terms of the total number of nodes
generated.
 To compare accuracy of the various algorithms,
the mean classification error on the test data sets
have been computed.

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Classification error for 10 data sets

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Nodes generated for 10 data
sets

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Results/Conclusion
 CDP has accuracy comparable to C4.5 while
generating considerably fewer nodes.
 CDP+ has accuracy comparable to C4.5 while
generating considerably fewer nodes.
 CDP+ outperformed CDP in error rate and
number of nodes generated.
 Considering all performance metrics together,
CDP+ is the best overall algorithm.
 Considering classification accuracy alone, C4.5P
is the winner.

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Conclusion
 Different datasets require different algorithms for
best results.
 Diverse user requirements put different
constraints on the final decision tree.
 The experiment shows that Interactive Visual
Data Mining Framework can help find the most
suitable algorithm for a given data set and group
of user requirements.

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Data Mining for Selective Visualization
of Large Spatial Datasets
Proceedings of 14th IEEE International Conference on Tools with
Artificial Intelligence
(ICTAI'02), 2002.
Washington (November 2002), DC, USA,
Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang, Rulin Liu
Computer Science & Engineering Department
University of Minnesota

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References
 http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-
users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekha
r02cubeview.pdf
 http://www.cs.umn.edu/Research/shashi-group/
 http://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdf
 http://www.cs.umn.edu/research/shashi-group/alan_planb.pdf
 http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27
637/http:zSzzSzwww-
users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/s
hekhar01detecting.pdf

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Basic Terminology
 Spatial databases
 Alphanumeric data + geographical cordinates
 Spatial mining
 Mining of spatial databases
 Spatial datawarehouse
 Contains geographical data
 Spatial outliers
 Observations that appear to be inconsistent with the
remainder of that set of data

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Contribution
 Propose and implement the CubeView
visualization system
 General data cube operations
 Built on the concept of spatial data warehouse to
support data mining and data visualization
 Efficient and scalable spatial outlier detection
algorithms

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Challenges in spatial data mining
 Classical data mining - numbers and categories.
Spatial data –
 more complex and
 extended objects such as points, lines and polygons.
 Second, classical data mining works with explicit
inputs, whereas spatial predicates and attributes
are often implicit.
 Third, classical data mining treats each input
independently of other inputs.

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Application Domain
 The Traffic Management Center - Minnesota
Department of Transportation (MNDOT) has a
database to archive sensor network.
 Sensor network includes
 about nine hundred stations
 each of which contains one to four loop detector
 Measurement of Volume and occupancy.
 Volume is # vehicles passing through station in 5-
minute interval
 Occupancy is percentage of time station is occupied
with vehicles

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Basic Concepts
 Spatial Data Warehouse
 Spatial Data Mining
 Spatial Outliers Detection

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Spatial Data Warehouse
 Employs data cube structure
 Outputs - albums of maps.
 Traffic data warehouse
 Measures - volume and occupancy
 Dimensions - time and space.

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Spatial Data Mining
 Process of discovering interesting and useful but
implicit spatial patterns.
 key goal is to partially ‘automate’ knowledge
discovery
 Search for “nuggets” of information embedded in
very large quantities of spatial data.

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Spatial Outliers Detection
 Suspiciously deviating observations
 Local instability
 Each Station
 Spatial attributes – time, space
 Non spatial attributes – volume, occupancy

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Basic Structure – CubeView

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CubeView Visualization System
 Each node in cube – a visualization style
 S - Traffic volume of station at all times.
 TTD – Time of the day
 TDW – Day of the week
 STTD – Daily traffic volume of each station
 TTD TDWS– Traffic volume at each station at different times
on different days

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Data Mining Algorithms for
Visualization
 Problem Definition
 Given a spatial graph G ={ S , E }
 S - s1, s2, s3, s4……..
 E – edges (neighborhood of stations)

 f ( x ) - attribute value for a data record
 N ( x )- fixed cardinality set of neighbors of x
 ) - Average attribute value of x neighbors
 S( x ) - difference of the attribute value of each data
object and the average attribute value of neighbors.

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Visualization
 Problem Definition cont…
 S( x ) - difference of the attribute value of each data
object and the average attribute value of neighbors.
 Test for detecting an outlier
 confidence level threshold θ



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Visualization
 Few points
 First, the neighborhood can be selected based on a fixed
cardinality or a fixed graph distance or a fixed Euclidean distance.
 Second, the choice of neighborhood aggregate function can be
mean, variance, or auto-correlation.
 Third, the choice for comparing a location with its neighbors can
be either just a number or a vector of attribute values.
 Finally, the statistic for the base distribution can be selected as
normal distribution.

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Visualization
 Algorithms
 Test Parameters Computation(TPC) Algorithm
 Route Outlier Detection(ROD) Algorithm

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Visualization

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Visualization

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Visualization

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Software
 http://www.cs.umn.edu/research/shashi-
group/vis/traffic_volumemap2.htm
 http://www.cs.umn.edu/research/shashi-
group/vis/DataCube.htm

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Visualization and Data Mining
techniques
Thank you!!!!

2. visualization in data mining

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2. visualization in data mining