PPT-A Simple Min-Cut Algorithm

Joseph Vessella RutgersCamden 1 The Problem Input Undirected graph G V E Edges have nonnegative weights Output A minimum cut of G 2 Cut Example Weight of this cut 11 Weight of min cut 4. 05 005 010 010 005 005 010 010 Cus Cus omer omer ehicle da ehicle da Da Da ac omple omple tion tion e e g St g St art art er er 10 char 10 char aceris aceris tic tic 15 c 15 oncep oncep ts ts 10 oper 10 oper ting poin ting poin ts ts 1s 1s ag ag 2s 2 99 4069 3629 3489 3339 3199 3159 3289 3429 3569 3969 4379 50 M Free 14309 13219 12119 11749 11389 11019 10999 11339 11689 12039 13089 14129 100 M Free 34849 32339 25839 25009 24169 23339 23039 23749 24469 25189 31329 33479 200 M Free 73729 65159 6058 perform Wirksam-keitenMin.Min.10 Min.Min.Min.Std.Std.VAH-ListeFl A Worst-Case Analysis. L. . Bertazzi. , B. Golden, and X. Wang. Route 2014. Denmark. June 2014 . 1. Introduction. In the min-sum VRP, the objective is to minimize the total cost incurred over all the routes. Chien. -chi Chen. 1. Outline. 2. Introduction. Interactive segmentation. Related work. Graph cut. Concept of graph cut. Hard and smooth constrains. Min cut/Max flow. Extensive of Graph cut. Grab cut. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. CS648. . Lecture . 25. Derandomization. using conditional expectation. A probability gem. 1. Derandomization. using . conditional expectation. 2. Problem 1. : Large cut in a graph. Problem:. Let . 4SanderJ.J.Leemans,DirkFahland,andWilM.P.vanderAalst (a)Lwith!cut (b)L1withcut (c)L2with^cut (d)L3with cut (e)DiscoveredPetrinetFigure2:Directly-followsgraphs.Dashedlinesdenotecuts.Edgeshavetheirfreq Ravishankar. . Krishnaswamy. Carnegie Mellon University. joint work with Nikhil . Bansal. (IBM) and . Anupam. Gupta (CMU). elgooG. : A Hypothetical Search Engine. Given a search query Q. Identify relevant webpages and order them. Dana . Moshkovitz. Computation. is Everywhere There’s . Data. and Something We Want to . Figure Out. .. Sometimes figuring out is done by hand. . Computation. enters when we ask how it . scales.  . Presented To: Prof. . Hagit. Hel-Or. Presented by: . Avner. And David. In the previews parts we have seen some kind of segmentation method. . In this lecture we will see graph cut, which is a another segmentation method based on a powerful mathematical tool.. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Find all frequent . itemsets. using . Apriori. and FB-growth.. List all of the strong association rules (with support s and confidence c) matching the following . metarule. , where X is a variable representing customers, and item . cutty. ”. This means taking out unnecessary pauses between actors’ delivery of dialogue lines. Sometimes it . means . tightening the gaps within dialogue sentences through the use of carefully placed cutaways. It may also mean losing redundant lines of dialogue, after the director has reviewed your cut..