Upload
kassia
View
52
Download
0
Tags:
Embed Size (px)
DESCRIPTION
TAILS: COBWEB 1 [1]. Online Digital Learning Environment for Conceptual Clustering. - PowerPoint PPT Presentation
Citation preview
TAILS: COBWEB1[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.
CMSI 401 COBWEB TAILS Enhancement 2
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
CMSI 401 COBWEB TAILS Enhancement 3
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
CMSI 401 COBWEB TAILS Enhancement 4
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
COBWEB Algorithm• What is COBWEB• How does COBWEB work
CMSI 401 COBWEB TAILS Enhancement 6
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
CMSI 401 COBWEB TAILS Enhancement 7
What is the COBWEB Algorithm?• Two tasks• Unsupervised
o No desired output for the input data
• Incrementalo Data stream
• Conceptualo Concept for each cluster
Discover the appropriate cluster for each input
Discover the concept for each cluster
CMSI 401 COBWEB TAILS Enhancement 8
How COBWEB Works
CMSI 401 COBWEB TAILS Enhancement 9
How COBWEB Works
CMSI 401 COBWEB TAILS Enhancement 10
CMSI 401 COBWEB TAILS Enhancement 11
Design
CMSI 401 COBWEB TAILS Enhancement 13
Requirements1. 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
CMSI 401 COBWEB TAILS Enhancement 14
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
CMSI 401 COBWEB TAILS Enhancement 15
Use Case Diagram (previous)
CMSI 401 COBWEB TAILS Enhancement 16
Use Case Diagram (revised)
CMSI 401 COBWEB TAILS Enhancement 17
State Chart Diagram (Behavioral View)
CMSI 401 COBWEB TAILS Enhancement 18
Package Diagram (Old Structure)
CMSI 401 COBWEB TAILS Enhancement 19
Package Diagram (New Structure)
CMSI 401 COBWEB TAILS Enhancement 20
Project Timeline
CMSI 401 COBWEB TAILS Enhancement 21
Responsibilities
Implementation
CMSI 401 COBWEB TAILS Enhancement 23
Clustering User Interface DesignFrom previous to Current
Designed and implemented by Robert “Quin” Thames, 2012
CMSI 401 COBWEB TAILS Enhancement 24
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
CMSI 401 COBWEB TAILS Enhancement 25
Project Justification• Developing a complex UI and back end
functionality has enhanced the abilities acquired from:- Interaction Design
- Algorithms
- Graphics
Vector Initialization GUI
CMSI 401 COBWEB TAILS Enhancement 26
CMSI 401 COBWEB TAILS Enhancement 27
Cluster GUI
CMSI 401 COBWEB TAILS Enhancement 28
Methods of Input• For adding attributes and values
• For adding nodes to tree
CMSI 401 COBWEB TAILS Enhancement 29
Action Log
CMSI 401 COBWEB TAILS Enhancement 30
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
CMSI 401 COBWEB TAILS Enhancement 31
Undo• Nodes are added or
removed in a group.• Add 10 random undo
causes the same 10 to disappear
CMSI 401 COBWEB TAILS Enhancement 32
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
CMSI 401 COBWEB TAILS Enhancement 33
Hover Text
CMSI 401 COBWEB TAILS Enhancement 34
Challenges• Working with Raphael.js• CSS Media Queries• Improving with the previous version of the
cluster• Parsing File Paste Input
CMSI 401 COBWEB TAILS Enhancement 35
Demonstration!Carlos and Miguel will now show a visual demonstration.
CMSI 401 COBWEB TAILS Enhancement 36
Questions? Concerns?
CMSI 401 COBWEB TAILS Enhancement 37
AcknowledgementsWe 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.