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Kruskals algorithm cluster merges as unions cluster

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Kruskal’s  Algorithm  Cluster merges  as unions  Cluster locations  as finds Running time  O (( n + m ) log n ) PQ operations  O ( m log n ) UF operations  O ( n log n ) Minimum Spanning Trees 10 Algorithm KruskalMST ( G ) Initialize a partition P for each vertex v in G do P.makeSet ( v ) let Q be a priority queue. Insert all edges into Q T { T is the union of the MSTs of the clusters} while T has fewer than n - 1 edges do e Q.removeMin () .getValue () [ u , v ] G.endVertices ( e ) A P.find ( u ) B P.find ( v ) if A B then Add edge e to T P.union ( A, B ) return T
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©  2010 Goodrich, Tamassia Minimum Spanning Trees 11 Prim-Jarnik’s Algorithm Similar to Dijkstra’s algorithm We pick an arbitrary vertex  s  and we grow the MST as  a cloud of vertices, starting from  s We store with each vertex  v  label  d ( v )  representing the  smallest weight of an edge connecting  v to a vertex in  the cloud  At each step: We add to the cloud the vertex  u  outside the cloud with the  smallest distance label We update the labels of the vertices adjacent to  u  
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©  2010 Goodrich, Tamassia Minimum Spanning Trees 12 Prim-Jarnik’s Algorithm (cont.) A heap-based adaptable  priority queue with  location-aware entries  stores the vertices  outside the cloud Key: distance Value: vertex Recall that method  replaceKey ( l,k )  changes  the key of entry  l We store three labels  with each vertex: Distance Parent edge in MST Entry in priority queue Algorithm PrimJarnikMST ( G ) Q new heap-based priority queue s a vertex of G for all v G.vertices () if v = s v.setDistance (0) else v.setDistance ( ) v.setParent ( ) l Q.insert ( v.getDistance () , v ) v.setLocator ( l ) while ¬ Q.empty () l Q.removeMin () u l.getValue () for all e u.incidentEdges () z e.opposite ( u ) r e.weight () if r < z.getDistance () z.setDistance ( r ) z.setParent ( e ) Q.replaceKey ( z.getEntry () , r )
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©  2010 Goodrich, Tamassia Minimum Spanning Trees 13 Example B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 8 B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 5 7 B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 5 7 B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 5 4 7
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©  2010 Goodrich, Tamassia Minimum Spanning Trees 14 Example (contd.) B D C A F E 7 4 2 8 5 7 3 9 8 0 3 2 5 4 7 B D C A F E 7 4 2 8 5 7 3 9 8 0 3 2 5 4 7
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©  2010 Goodrich, Tamassia Minimum Spanning Trees 15 Analysis Graph operations
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