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TAILS: COBWEB 1 [1] Online Digital Learning Environment for Conceptual Clustering This material is based upon work supported by the National Science Foundation.

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Presentation on theme: "TAILS: COBWEB 1 [1] Online Digital Learning Environment for Conceptual Clustering This material is based upon work supported by the National Science Foundation."— Presentation transcript:

1 TAILS: COBWEB 1 [1] Online Digital Learning Environment for Conceptual Clustering This material is based upon work supported by the National Science Foundation under Course, Curriculum, and Laboratory Improvement (CCLI) Grant No 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.

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 2

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 3

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 CMSI 401 COBWEB TAILS Enhancement 4

5 COBWEB Algorithm What is COBWEB How does COBWEB work

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 6

7 What is the COBWEB Algorithm? Two tasks Unsupervised o No desired output for the input data Incremental o Data stream Conceptual o Concept for each cluster Discover the appropriate cluster for each input Discover the concept for each cluster CMSI 401 COBWEB TAILS Enhancement 7

8 How COBWEB Works CMSI 401 COBWEB TAILS Enhancement 8

9 How COBWEB Works CMSI 401 COBWEB TAILS Enhancement 9

10 10

11 CMSI 401 COBWEB TAILS Enhancement 11

12 Design

13 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 CMSI 401 COBWEB TAILS Enhancement 13

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 14

15 CMSI 401 COBWEB TAILS Enhancement 15 Use Case Diagram (previous )

16 CMSI 401 COBWEB TAILS Enhancement 16 Use Case Diagram (revised )

17 State Chart Diagram (Behavioral View) CMSI 401 COBWEB TAILS Enhancement 17

18 Package Diagram (Old Structure) CMSI 401 COBWEB TAILS Enhancement 18

19 Package Diagram (New Structure) CMSI 401 COBWEB TAILS Enhancement 19

20 Project Timeline CMSI 401 COBWEB TAILS Enhancement 20

21 Responsibilities CMSI 401 COBWEB TAILS Enhancement 21

22 Implementation

23 Clustering User Interface Design From previous to Current CMSI 401 COBWEB TAILS Enhancement 23 Designed and implemented by Robert “Quin” Thames, 2012

24 Implement an Intuitive and Responsive UI Ad apt the application to the TAILS project Make it possible to port the application use across devices Implement new functionality Create an overall more elega n t look CMSI 401 COBWEB TAILS Enhancement 24

25 Project Justification Developing a complex UI and back end functionality has enhanced the abilities acquired from: -Interaction Design -Algorithms -Graphics CMSI 401 COBWEB TAILS Enhancement 25

26 Vector Initialization GUI CMSI 401 COBWEB TAILS Enhancement 26

27 Cluster GUI CMSI 401 COBWEB TAILS Enhancement 27

28 Methods of Input For adding attributes and values For adding nodes to tree CMSI 401 COBWEB TAILS Enhancement 28

29 Action Log CMSI 401 COBWEB TAILS Enhancement 29

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 30

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 31

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 32

33 Hover Text CMSI 401 COBWEB TAILS Enhancement 33

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 34

35 Demonstration! Carlos and Miguel will now show a visual demonstration. CMSI 401 COBWEB TAILS Enhancement 35

36 Questions? Concerns? CMSI 401 COBWEB TAILS Enhancement 36

37 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: /BF "Knowledge acquisition via incremental conceptual clustering". Machine Learning 2 (2): 139–172.doi: /BF CMSI 401 COBWEB TAILS Enhancement 37


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