Visualization and Data Mining techniques

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Visualization and Data Mining techniques

• By-
• Group number- 14
• Chidroop Madhavarapu(105644921)
• Deepanshu Sandhuria(105595184)
• Data Mining CSE 634
• Prof. Anita Wasilewska

2
References

• http//coblitz.codeen.org3125/citeseer.ist.psu.ed
  u/cache/papers/cs/10335/ftpzSzzSzftp.cs.umn.eduzS
  zdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual
  .pdf
• http//www.ailab.si/blaz/predavanja/ozp/gradivo/20
  02-Keim-Visualization20in20DM-IEEE20Trans20Vis
  .pdf
• http//www.geocities.com/anand_palm/
• http//citeseer.ist.psu.edu/cache/papers/cs/27216/
  httpzSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPape
  rTalkFilezSzits02.pdf/shekhar02cubeview.pdf
• http//www.cs.umn.edu/Research/shashi-group/
• http//www.cs.umn.edu/Research/shashi-group/Book/s
  db-chap1.pdf
• http//www.cs.umn.edu/research/shashi-group/alan_p
  lanb.pdf
• http//coblitz.codeen.org3125/citeseer.ist.psu.ed
  u/cache/papers/cs/27637/httpzSzzSzwww-users.cs.um
  n.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/she
  khar01detecting.pdf

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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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Why Visual Data Mining
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Why Visual Data Mining
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VDM Approach

• VDM takes advantage of both,
• The power of automatic calculations, and
• The capabilities of human processing.
• Human perception offers phenomenal abilities to
  extract structures from pictures.

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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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Histogram
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Pie Chart
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Bivariate Data

• Constitutes of paired samples of two quantitative
  variables
• Variables are related
• Represented using following methods
• Scatter plots
• Line graphs

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Scatter plots
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Line graphs
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Multivariate Data

• Multi dimensional representation of multivariate
  data
• Represented using following methods
• Icon based methods
• Pixel based methods
• Dynamic parallel coordinate system

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Icon based Methods
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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.org3125/citeseer.ist.psu.ed
  u/cache/papers/cs/10335/ftpzSzzSzftp.cs.umn.eduzS
  zdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual
  .pdf
• http//www.ailab.si/blaz/predavanja/ozp/gradivo/20
  02-Keim-Visualization20in20DM-IEEE20Trans20Vis
  .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
  users 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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Search Process
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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 lt
  acceptable error rate.
• A3) Total number of model atoms in the model
  candidategt 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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Final decision tree size
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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/
  httpzSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPape
  rTalkFilezSzits02.pdf/shekhar02cubeview.pdf
• http//www.cs.umn.edu/Research/shashi-group/
• http//www.cs.umn.edu/Research/shashi-group/Book/s
  db-chap1.pdf
• http//www.cs.umn.edu/research/shashi-group/alan_p
  lanb.pdf
• http//coblitz.codeen.org3125/citeseer.ist.psu.ed
  u/cache/papers/cs/27637/httpzSzzSzwww-users.cs.um
  n.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/she
  khar01detecting.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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Spatial Cluster
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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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Dimension Lattice
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CubeView Visualization System
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CubeView Visualization System
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CubeView Visualization System
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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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Data Mining Algorithms for 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 ?

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Data Mining Algorithms for 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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Data Mining Algorithms for Visualization

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

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Data Mining Algorithms for Visualization
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Data Mining Algorithms for Visualization
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Data Mining Algorithms for Visualization
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Software

• http//www.cs.umn.edu/research/shashi-group/vis/tr
  affic_volumemap2.htm
• http//www.cs.umn.edu/research/shashi-group/vis/Da
  taCube.htm

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Visualization and Data Mining techniques

• Thank you!!!!