Algorithm for ranking and grouping properties of a set of ranked articles.

GroupingAlgorithm.pseudo

Grouping Algorithm ('ranked articles') =
'input groups' : [{'Property Names'}] <- trivially preprocess 'ranked articles'
'set of all graphs' <- 'Identify separate graphs' using 'input groups'
groups : {Group} <- empty
for each graph in 'set of all graphs'
  'spanning tree' <- 'Find minimal spanning tree' of graph
  groups += 'Split spanning tree' 'spanning tree' into groups
groups <- merge groups smaller than (g_min : Int)
'ranked groups' <- rank groups by average scoring
return 'ranked groups'

Identify separate graphs ('input groups') =
for each group in 'input groups'
  'graphs to merge' <- in 'set of graphs' 'take out graphs containing any field'* of g
  new graph <- merge* 'graphs to merge' using group
  add new graph to 'set of graphs'
return 'set of graphs'

Split spanning tree ('spanning tree') =
node <- random node of 'spanning tree'
groups : {Group}
while 'spanning tree' is not empty
  group : Group = empty
  while 'spanning tree' is not empty and group < g_max
    group += 'take neighbour with least concecutive neighbours'* from 'spanning tree'
  if group.size not 0
    groups += group
return groups

'Find minimal spanning tree' = Kruskal*

------
Requirements induced on data structures:
spanning tree: 
  for each node:
    ranking by number of neighbours (treemap : (node:NrNeighbours))
property name:
  name : String
  relevancy score : Num

Meaning:
'ident 1' -> ident_1
ident* = Definition missing
------