Transcript Conceptual Clustering Activity CMSI 401 presentation

TAILS: COBWEB
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[1]
Online Digital Learning Environment for
Conceptual Clustering
ⱡ
This material is based upon work supported by the National Science Foundation under Course, Curriculum, and
Laboratory Improvement (CCLI) Grant No. 0942454. Any opinions, findings and conclusions or recommendations
expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science
Foundation.
Meet The Team
● Carlos
o Senior CMSI Major, 401 Project
● Liyang
o MSEE Graduate Student
● Poulomi
o Graduate Student
● Michael
o EE Senior working with TAILS
● Miguel
o EE Senior working with TAILS
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Motivation
● Chemistry, Biology, Physics
○
all have lectures and labs
■ lectures provide concepts
■ labs provide hands-on and visual experience
● Artificial Intelligence
○
Traditionally taught with large arrays of algorithms at a
conceptual level
■ little hands-on experience and low levels of coding
○ Or one to two algorithms taught with large projects
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Project Overview
● TAILS Goal
○
Develop complete applications with embedded algorithms
■ Will allow students to study and experiment with the
application
■ Will allow students to implement and enhance AI aspects
of the application
● Module Goal
○
Develop a complete application depicting the COBWEB
Conceptual Clustering algorithm
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COBWEB Algorithm
• What is COBWEB
• How does COBWEB work
What is the COBWEB Algorithm?
• Unsupervised
○ No desired output for the input data
• Incremental
○ Data stream
• Conceptual
○ Concept for each cluster
• Polythetic
○ Evaluation on all of the observation's attribute-values
rather than a single one
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What is the COBWEB Algorithm?
• Two tasks
• Unsupervised
•
o No desired output for
the input data
Incremental
•
o Data stream
Conceptual
Discover the appropriate
cluster for each input
Discover the concept
for each cluster
o Concept for each cluster
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How COBWEB Works
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How COBWEB Works
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Design
Requirements
1. The system shall initialize depending on the user inputs
2. The system shall allow the user with options to add feature vectors to the tree
3. The system shall display the results such that the user can understand
working of the algorithm
4. The system shall have a feature of backtracking to previous working stages
5. The systems shall provide the user with an option to view diverse set of
representations of the clustered tree generated.
6. The system shall have project documentation that will be maintained by
assigned team member
7. The system shall be verified using test cases developed by assigned team
member
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Design
• Functional View - focuses on the functional
requirements. No specific implementation details
• Behavioral View - focuses on the behavior of working
of the system.
• Structural View - focuses on the structure of intended
implementation
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Use Case Diagram (previous)
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Use Case Diagram (revised)
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State Chart Diagram (Behavioral View)
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Package Diagram (Old Structure)
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Package Diagram (New Structure)
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Project Timeline
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Responsibilities
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Implementation
Clustering User Interface Design
From previous
to
Current
Designed and implemented by
Robert “Quin” Thames, 2012
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Implement an Intuitive and
Responsive UI
• Adapt the application to the
TAILS project
• Make it possible to port the
application use across
devices
• Implement new functionality
• Create an overall more
elegant look
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Project Justification
• Developing a complex UI and back end
functionality has enhanced the abilities acquired
from:
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Interaction Design
-
Algorithms
-
Graphics
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Vector Initialization GUI
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Cluster GUI
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Methods of Input
• For adding attributes and values
• For adding nodes to tree
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Action
Log
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Undo
• Unable to go back to previous state
• Able to go back by up to three phases
• To remake a tree as previously made, need to
re-input each node
Algorithm produces same tree if nodes are input in same
order
- Takes longer to produce larger trees
-
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Undo
• Nodes are added or
removed in a group.
• Add 10 random undo
causes the same 10 to
disappear
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Hover Text
• Tree statistics used to appear only when a node
was clicked on
-
Would appear as an alert dialog requiring the user to close
it
• A text box will now appear below the node when
the user hovers over it
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Hover
Text
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Challenges
• Working with Raphael.js
• CSS Media Queries
• Improving with the previous version of the
cluster
• Parsing File Paste Input
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Demonstration!
Carlos and Miguel will now show a visual
demonstration.
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Questions? Concerns?
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Acknowledgements
We are grateful to Quin Thames for implementing the original
version of the COBWEB algorithm. While we redesign the user
interface, Quin’s implementation of the the category utility
function remains at the heart of the module.
We are also grateful to Doug Fisher for publishing such a
fascinating clustering algorithm.
[1] Fisher, Douglas (1987). "Knowledge acquisition via incremental conceptual
clustering". Machine Learning 2 (2): 139–172.doi:10.1007/BF00114265.
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