The goal of the project is to get an example with metric datas which is based on the article : " Performance guarantee for hierarchical clustering " authors : Dasgupta and Long,
you can also find this article on line : [login to view URL]~dasgupta/papers/[login to view URL]
Deadline is 7th september 2018
This jobs suits to Dataminers, Data scientist , machine learning Specialist, Statisticians.....
The goal of the project is to find metric datas and applying Dasgupta hierarchical clustering algorithm : first step is to apply Farthest First Traversal algorithm on metric data : author Gonzalez : then we get a tree and then applying Dasgupta algorithm on this tree of Farthest First Traversal algorithm: we must have better or optimal clustering by getting shorter distance with Dasgupta algorithm.
For this project : any distance can be used except euclidian distance
Distance that can be used are : Lorentzian or Jaccard or Canbera , or any other distance etc..... But the most important thing is to get a shorter distance when applying Dasgupta Algorithm on the tree of Farthest First Traversal algorithm : if the results are greater distance than Farthest First Traversal or equal distance : so that these results ( greater or equal ) don't show that Dasgupta Algorithm is better than Farthest First Traversal. When applying Dasgupta Algorithm ( second step ) : it is mandatory to get a shorter distance between two points on a metric space . ( please also attached txt file : important notice goal [login to view URL] )
In attached file to this mail : you can find a bad example : [login to view URL] preliminaire or new rapport preliminaire : but in these examples : distances are greater or equal to distances obtained with Farthest First Traversal algorithm. These documents are written in French , if needed, I can translated in English.
Could you manage to get a practical example where distance are shorter when applying Dasgupta Algorithm on the results of Farthest First Traversal algorithm ?
I'm looking forward hearing from you.
Have a nice day