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  • 1. 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.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdf
    • http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdf
    • http:// www.geocities.com/anand_palm /
    • http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.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/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf
  • 3. 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.
  • 4. Why Visual Data Mining
  • 5. Why Visual Data Mining
  • 6. 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.
  • 7. 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.
  • 8. Methods of Data Visualization
    • Different methods are available for visualization of data
    • based on type of data
    • Data can be
    • Univariate
    • Bivariate
    • Multivariate
  • 9. Univariate data
    • Measurement of single quantitative variable
    • Characterize distribution
    • Represented using following methods
      • Histogram
      • Pie Chart
  • 10. Histogram
  • 11. Pie Chart
  • 12. Bivariate Data
    • Constitutes of paired samples of two quantitative variables
    • Variables are related
    • Represented using following methods
      • Scatter plots
      • Line graphs
  • 13. Scatter plots
  • 14. Line graphs
  • 15. Multivariate Data
    • Multi dimensional representation of multivariate data
    • Represented using following methods
      • Icon based methods
      • Pixel based methods
      • Dynamic parallel coordinate system
  • 16. Icon based Methods
  • 17. 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
  • 18. 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.
  • 19. 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.
  • 20. References
    • http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdf
    • http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdf
    • http:// www.geocities.com/anand_palm /
  • 21. 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.
  • 22. Components of VQLBCI
    • The three major components of VQLBCI are Visual Representations, Computations and Events.
  • 23. 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.
  • 24. ER diagram for the search space of decision tree learning algorithm
  • 25. 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.
  • 26. 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.
  • 27. Search Process
  • 28. 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
  • 29. 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
  • 30. 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.
  • 31. 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
  • 32. 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.
  • 33. 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.
  • 34. Comparison of different classification learning algorithms
  • 35. 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.
  • 36. Classification error for 10 data sets
  • 37. Nodes generated for 10 data sets
  • 38. Final decision tree size
  • 39. 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.
  • 40. 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.
  • 41. 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
  • 42. References
    • http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.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/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf
  • 43. 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
  • 44. Spatial Cluster
  • 45. 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
  • 46. 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.
  • 47. 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
  • 48. Basic Concepts
    • Spatial Data Warehouse
    • Spatial Data Mining
    • Spatial Outliers Detection
  • 49. Spatial Data Warehouse
    • Employs data cube structure
    • Outputs - albums of maps.
    • Traffic data warehouse
      • Measures - volume and occupancy
      • Dimensions - time and space.
  • 50. 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.
  • 51. Spatial Outliers Detection
    • Suspiciously deviating observations
    • Local instability
    • Each Station
      • Spatial attributes – time, space
      • Non spatial attributes – volume, occupancy
  • 52. Basic Structure – CubeView
  • 53. CubeView Visualization System
    • Each node in cube – a visualization style
      • S - Traffic volume of station at all times.
      • T TD – Time of the day
      • T DW – Day of the week
      • ST TD – Daily traffic volume of each station
      • T TD T DW S– Traffic volume at each station at different times on different days
  • 54. Dimension Lattice
  • 55. CubeView Visualization System
  • 56. CubeView Visualization System
  • 57. CubeView Visualization System
  • 58. 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.
  • 59. 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 θ
  • 60. 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.
  • 61. Data Mining Algorithms for Visualization
    • Algorithms
      • Test Parameters Computation(TPC) Algorithm
      • Route Outlier Detection(ROD) Algorithm
  • 62. Data Mining Algorithms for Visualization
  • 63. Data Mining Algorithms for Visualization
  • 64. Data Mining Algorithms for Visualization
  • 65. Software
    • http://www.cs.umn.edu/research/shashi-group/vis/traffic_volumemap2.htm
    • http://www.cs.umn.edu/research/shashi-group/vis/DataCube.htm
  • 66. Visualization and Data Mining techniques Thank you!!!!

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