This document describes Pathfinder, a graphic-theoretic technique that forms networks from proximity data. Pathfinder takes proximity matrices and produces networks where nodes represent concepts and links represent relationships between concepts. It uses two parameters, q and r, to determine the number and distance of links in the generated network. Pathfinder is useful for knowledge elicitation, data visualization, and analyzing citation patterns.

1. PATH FINDER
Presented By:
Group E
(2070-MSCS: Govinda, Sunena, Love Kumar,Yogendra)

2. Outline
•Introduction and Background
•Description
•Pathfinder Algorithms
•Examples
•Summary
•References

3. Introduction & Background
Proximity Data
-- Similarities
-- Relatedness
-- Association between entities

4. Methods of Proximity DataAnalysis
-- Multidimensional Scaling
--Hierarchical Clusters

5. GraphTheory
Nodes and Links
•Node==Finite set(1,2,…..n)
•Links==Subset of set of all node pairs

6. Node
Links
Node
Weight W

7. Directed and Undirected Graphs
•Directed
•Undirected

8. • Derived from proximities for pairs of entities
• Proximity ????
Similarities, Correlations, distances, conditional probabilities
or any other relationship among the entities
• Knowledge ElicitationTechnique developed around mid
1980s
Path Finder

9. Path finder Parameters
q parameter
-- n=Number of nodes or items
-- 2 to n-1

10. PFNet(q, r)
r parameter
-- Distance of path(Minkowski distance)
-- 1 to infinity

11. Path Finder
-- Pathfinder algorithm takes
proximity matrices and produces
networks in which nodes are concepts
and links are relations between them.

12. PFNet(q, r)
--The PFnet(n − 1, ∞) includes all of the links in any
minimum spanning tree
(q, r) no. of links

13. Example

14. Example
-- Cottonwood, plants and leaves
are closely related each other.
They overlap each other
-- Flower, green and tree also
seem to be closer.They are
connected to the plant.
-- Green is closest concept to
plant because it has shortest link
to the plant

15. Summary
• Pathfinder is graphic-theoretic technique that forms network from proximity
data.
• Concepts are represented by nodes.Their relationship is represented by link
between them.
• Can be used in knowledge acquisition, data visualization, citation patterns etc.

16. References:
1. http://en.wikipedia.org/wiki/Pathfinder_network
2. http://interlinkinc.net/Pathfinder.html
3. http://iv.slis.indiana.edu/sw/pfnet.html
4. Schvaneveldt, R.W., Durso, F.T., & Dearholt, D.W. (1989). Network
structures in proximity data. In G. Bower (Ed.),The psychology of learning
and motivation: Advances in research and theory,Vol. 24 (pp. 249–284).
NewYork: Academic Press